Colour coding for different time intervals within a plot

I need different colours for different time intervals within my figure. so the x axis is a time vector, that is a cell of combinations of different doubles. the coding for this is shown below:

%Number of Positions and Impacts
Impacts = [50, 60, 75, 375, 420, 360];
Total_Number_of_Impacts = sum(Impacts);
Cumalative_Sum_of_Impacts = cumsum(Impacts);

%Time
Time = 5400;                   
Interval = 900;
Time_Interval_Vector = 0:Interval:Time;

j = numel(Time_Interval_Vector);

Time_Vector_Pieces = cell(1, j-1);
for Iter = 1:j-1
    Inc = Interval/Impacts(Iter);
    Time_Vector_Pieces{Iter} = Time_Interval_Vector(Iter):Inc:(Time_Interval_Vector(Iter+1)-Inc);
end
Time_Vector = [Time_Vector_Pieces{:}];

As you can see from the variable Time_Vector_Pieces this is where all the different doubles are stored each representing a time inerval. so what i need is for each of these pieces / intervals to be shown as a different colour on my figure to be able to differentiate between each time interval.

Source_Level = zeros(1,Total_Number_of_Impacts);

for i = 1:Total_Number_of_Impacts

    %Source Level Step Up
    if i<=Cumalative_Sum_of_Impacts(1)
        Source_Level(i) = 200 ;  
    else
        Source_Level(i) = 210; 
    end
end


%RECEIVE LEVEL MODEL
figure
set(plot(Time_Vector,Source_Level,'.'),'markersize',3)
title('Sound Exposure Graph (SEL)')
xlabel('Time (s)')
ylabel('Sound Exposure Level (SEL) (dB)')
legend({'Individual Strike Recieve Level (dB re μPa^2s)','Individual Strike Source Level (dB re μPa^2s-m)','Cumalative SEL Level (dB re μPa^2s)'},'Location','east')

Warning: Ignoring extra legend entries.

SN: i havent included all the plots on the figure as the code would be too long. i will just assume for the other plots it would be the same method

Answer 1

[Adil Saeed]

plot(Time_Vector(1:Cumalative_Sum_of_Impacts(1)),Source_Level(1:Cumalative_Sum_of_Impacts(1)),'b.','markersize',3)
plot(Time_Vector(Cumalative_Sum_of_Impacts(1):Cumalative_Sum_of_Impacts(2)),Source_Level(Cumalative_Sum_of_Impacts(1):Cumalative_Sum_of_Impacts(2)),'g.','markersize',3)
plot(Time_Vector(Cumalative_Sum_of_Impacts(2):Cumalative_Sum_of_Impacts(3)),Source_Level(Cumalative_Sum_of_Impacts(2):Cumalative_Sum_of_Impacts(3)),'r.','markersize',3)
plot(Time_Vector(Cumalative_Sum_of_Impacts(3):Cumalative_Sum_of_Impacts(4)),Source_Level(Cumalative_Sum_of_Impacts(3):Cumalative_Sum_of_Impacts(4)),'c.','markersize',3)
plot(Time_Vector(Cumalative_Sum_of_Impacts(4):Cumalative_Sum_of_Impacts(5)),Source_Level(Cumalative_Sum_of_Impacts(4):Cumalative_Sum_of_Impacts(5)),'m.','markersize',3)
plot(Time_Vector(Cumalative_Sum_of_Impacts(5):Cumalative_Sum_of_Impacts(6)),Source_Level(Cumalative_Sum_of_Impacts(5):Cumalative_Sum_of_Impacts(6)),'y.','markersize',3)

I found a very long winded approach for those interested