clc
clear all
close all
%%
xx=1 ;
%%
Data = csvread('radius6_misaligned_v_Abeta0_stimulateBOTH_edgedist0.1_.csv');
time = Data(:,1) ;
[satr, soton] = size(Data) ;
figure
for i=2:soton
plot(time , Data(:,i), '-b', 'LineWidth' , 1 )
hold on
end
%xlim([0 5])
ylim([-90 40])
Data1 = Data(:,2:soton) ; %exclude time from the data
%%
soton=soton-1 ;
number_nodes = soton ;
%%
n = 1 ;
s=n+1:number_nodes-n ;
S1 = zeros(1,length(s)) ;
k=1 ;
for j=n+1:number_nodes-n
clear V
clear A AA1 AA2 pks locs L1 L2 B1 B2
V = Data1(:,j) ;
[pks , locs] = findpeaks(V,'MinPeakDistance',400, 'MinPeakHeight',0) ;
for i=1:length(pks)
A(1,:) = V(locs(i,1)-30:locs(i,1) , 1) ;
AA1 = A(A>=-20) ;
[L1]=knnsearch(AA1',-20) ;
B1 = AA1(1,L1) ;
AA2 = A(A<=-20) ;
[L2]=knnsearch(AA2',-20) ;
B2 = AA2(1,L2) ;
[row1]=find(V==B1) ;
[row2]=find(V==B2) ;
if length(row1)>1
for l=1:length(row1)
if row1(l) > locs(i,1)-30 && row1(l) < locs(i,1)
row11 = row1(l) ;
end
end
else
row11 = row1 ;
end
if length(row2)>1
for l=1:length(row2)
if row2(l) > locs(i,1)-30 && row2(l) < locs(i,1)
row22 = row2(l) ;
end
end
else
row22 = row2 ;
end
T1 = time(row11,1) ;
T2 = time(row22,1) ;
coefficients = polyfit([T1, T2], [B1, B2], 1);
a = coefficients (1);
b = coefficients (2);
f = -20 ;
x = (f-b)/a ;
S1(1,k) = x ;
plot(x,-20 , '*r')
end
k=k+1 ;
end
%%
figure
for i=1:length(S1)
plot(S1(1,i) , 1 , '.', 'Color','none')
text(S1(1,i), 1, '|', 'VerticalAlignment','middle', 'HorizontalAlignment','center', 'Color','m')
hold on
end
ylim([0 5])
%% CV
Y = (29.9276800000000 * xx )*40 + (3 * xx) *2 + (10.1532800000000 *xx ) * 5 * 2 ;
nodes_dist = Y+1 ;
for i=1:length(S1)-1
R1 = S1(1,i+1)-S1(1,i) ; %ms
R1=R1/1000 ; %s
CV(1,i) = (nodes_dist*1e-6)/ R1 ; %m/s
end
%%
figure
plot(s(1,1:length(s)-1) , CV , 'o-m')
xlim([0 50])
title('CV')
xlabel('node #')
ylabel('CV')
%% average CV
mean_CV = mean(CV(1,10:20))