CatmaidNeuronTools/PlotDenDists.m at master · Lathomas42/CatmaidNeuronTools · GitHub

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load('dendDists.mat')
n = length(denDists(:,1));
% Get denDists from python newVectors.getDenDists(cons)
DoriDspdDendDif = [] % <Dori><Dspeed><DendDist><DiffDendrite(color)><size><sk1><sk2>
skelPairs = []
for i = 1:n
    sk1= denDists(i,2);
    sk2 = denDists(i,3);
    if sk1 ~= sk2
        ori1 = EMidOriRGBneuronIDSFTFspeed(EMidOriRGBneuronIDSFTFspeed(:,1) == sk1,2);
        ori2 = EMidOriRGBneuronIDSFTFspeed(EMidOriRGBneuronIDSFTFspeed(:,1) == sk2,2);
        dori = abs(ori1-ori2);
        if dori >90
            dori = 180 - dori;
        end
        spd1 = EMidOriRGBneuronIDSFTFspeed(EMidOriRGBneuronIDSFTFspeed(:,1)== sk1, 9);
        spd2 = EMidOriRGBneuronIDSFTFspeed(EMidOriRGBneuronIDSFTFspeed(:,1)== sk2, 9);
        dspd = abs(spd1-spd2);
        DoriDspdDendDif = [DoriDspdDendDif; [dori, dspd, denDists(i,4), denDists(i,5), sk1, sk2]];
    end
end

DoriDspdDendDif1Den = DoriDspdDendDif(DoriDspdDendDif(:,4)==0,:)

uniqueSkelPairs = unique(DoriDspdDendDif1Den(:,5:6),'rows')
%now code to make a averaged DoriDspdDendDifSize
n2 = length(uniqueSkelPairs);
% will have <sk1><sk2><doriAVG><dspdAVG><DenDistAVG><n_pairs>
DoriDspdDendDifAveraged = [uniqueSkelPairs zeros(n2,4)]
n3 = length(DoriDspdDendDif1Den(:,1))
for j = 1:n2
    for k = 1:n3
        if(DoriDspdDendDif1Den(k,5:6) == uniqueSkelPairs(j,:))
            DoriDspdDendDifAveraged(j,3) = DoriDspdDendDifAveraged(j,3)+DoriDspdDendDif1Den(k,1);
            DoriDspdDendDifAveraged(j,4) = DoriDspdDendDifAveraged(j,4)+DoriDspdDendDif1Den(k,2);
            DoriDspdDendDifAveraged(j,5) = DoriDspdDendDifAveraged(j,5)+DoriDspdDendDif1Den(k,3);
            DoriDspdDendDifAveraged(j,6) = DoriDspdDendDifAveraged(j,6)+1;
        end
    end
end
% now take arithemteic averages of the sums
DoriDspdDendDifAveraged(:,3) = DoriDspdDendDifAveraged(:,3)./DoriDspdDendDifAveraged(:,6);
DoriDspdDendDifAveraged(:,4) = DoriDspdDendDifAveraged(:,4)./DoriDspdDendDifAveraged(:,6);
DoriDspdDendDifAveraged(:,5) = DoriDspdDendDifAveraged(:,5)./DoriDspdDendDifAveraged(:,6);

%% Fig of nonaveraged non parsed data
figure
subplot(2,1,1)
hold on

scatter(DoriDspdDendDif(:,1),DoriDspdDendDif(:,3),20,DoriDspdDendDif(:,4))
title('dori vs dist between connectors')
b=subplot(2,1,2)
hold on
scatter(DoriDspdDendDif(:,2),DoriDspdDendDif(:,3),20,DoriDspdDendDif(:,4))
title('dspd vs dist between connectors')

%% Fig of nonaveraged data including only cons on one Dendrite
figure
subplot(2,1,1)
hold on

scatter(DoriDspdDendDif1Den(:,1),DoriDspdDendDif1Den(:,3),20,DoriDspdDendDif1Den(:,4))
title('dori vs dist between connectors')

subplot(2,1,2)
hold on
scatter(DoriDspdDendDif1Den(:,2),DoriDspdDendDif1Den(:,3),20,DoriDspdDendDif1Den(:,4))
title('dspd vs dist between connectors')

%% Least Squares for the oneDendrite nonaveraged data
x1 = DoriDspdDendDif1Den(:,1)
y1 = DoriDspdDendDif1Den(:,3)
coeffs1 = polyfit(x1, y1, 1);
% Get fitted values
fittedX1 = linspace(min(x1), max(x1), 200);
fittedY1 = polyval(coeffs1, fittedX1);

x2 = DoriDspdDendDif1Den(:,2)
y2 = DoriDspdDendDif1Den(:,3)
coeffs2 = polyfit(x2, y2, 1);
% Get fitted values
fittedX2 = linspace(min(x2), max(x2), 200);
fittedY2 = polyval(coeffs2, fittedX2);

%% Fig of averaged data including only cons on one Dendrite
figure
subplot(2,1,1)
hold on
scatter(DoriDspdDendDifAveraged(:,3),DoriDspdDendDifAveraged(:,5),DoriDspdDendDifAveraged(:,6)*5)
title('dori vs dist between connectors')

subplot(2,1,2)
hold on
scatter(DoriDspdDendDifAveraged(:,4),DoriDspdDendDifAveraged(:,5),DoriDspdDendDifAveraged(:,6)*5)
title('dspd vs dist between connectors')