Can graphs in Matlab provide a more detailed scale?

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cindyawati cindyawati on 19 Jul 2023

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https://au.mathworks.com/matlabcentral/answers/1998078-can-graphs-in-matlab-provide-a-more-detailed-scale

Answered: Steven Lord on 19 Jul 2023

I have 2 different function and I want to combine the plot. In Matlab cannot show the difference plot of 2 function but in excel can show the difference. How to solve that?

This is the function that has been combined

clc;clear;

%input parameter

delta=50;

gamma=75;

K1=10^-4;

K2=5*10^-4;

K3=10^-3;

K4=5*10^-3;

K5=10^-2;

K6=5*10^-2;

Ko=0.1;

n=6;

Oa=10;

Pa=100;

mu_1=10^-3;

mu_2=10^-3;

mu_3=10^-3;

mu_4=10^-3;

mu_5=10^-3;

mu_6=10^-3;

mu_o=10^-4;

mu_p= 10^-5;

%komponen paper 1

Mg = 0.047;

lambdaMf = 2*10^-1;

Fo = 3.36*10^-5;

KFo = 2.58*10^-5;

lambdaMa = 2.3*10^-1;

Ao = 0.14;

KAo = 10^-2;

beta = 10;

epsilon1 = 0.3333;

epsilon2 = 0.8;

lambdaM1Tb = 6*10^-1;

Tb=10^-2;

KTb = 2.5*10^-3;

makro1 = 0.02;

makro2 = 0.02;

dM1= 0.015;

dM2= 0.015;

dtmakro1 = (Mg*(lambdaMf*(Fo/(Fo+KFo))+lambdaMa*(Ao/(Ao+KAo)))*(epsilon1*beta/(beta*(epsilon1+epsilon2)))*makro1)+(lambdaM1Tb*(Tb/(Tb+KTb))*makro1)-dM1*makro1;

dtmakro2 = (Mg*(lambdaMf*(Fo/(Fo+KFo))+lambdaMa*(Ao/(Ao+KAo)))*(epsilon2/(beta*(epsilon1+epsilon2)))*makro2)+(lambdaM1Tb*(Tb/(Tb+KTb))*makro1)-dM2*makro2;

%input initial condition

M1(1)=10;

M2(1)=0;

M3(1)=0;

M4(1)=0;

M5(1)=0;

M6(1)=0;

O(1)=0;

P(1)=0;

M12(1)=10;

M22(1)=0;

M32(1)=0;

M42(1)=0;

M52(1)=0;

M62(1)=0;

O2(1)=0;

P2(1)=0;

%input for time

t(1)=0;

dt=0.01; %time interval

t=0:dt:100; %time span

%input empty array

T=zeros(length(t)+1,1); %empty array for t

M1=zeros(length(t)+1,1); %empty array for M1

M2=zeros(length(t)+1,1); %empty array for M2

M3=zeros(length(t)+1,1); %empty array for M3

M4=zeros(length(t)+1,1); %empty array for M4

M5=zeros(length(t)+1,1); %empty array for M5

M6=zeros(length(t)+1,1); %empty array for M6

O=zeros(length(t)+1,1);

P=zeros(length(t)+1,1);

M12=zeros(length(t)+1,1); %empty array for M1

M22=zeros(length(t)+1,1); %empty array for M2

M32=zeros(length(t)+1,1); %empty array for M3

M42=zeros(length(t)+1,1); %empty array for M4

M52=zeros(length(t)+1,1); %empty array for M5

M62=zeros(length(t)+1,1); %empty array for M6

O2=zeros(length(t)+1,1);

P2=zeros(length(t)+1,1);

sumter=K2*M2+K3*M3+K4*M4+K5*M5;

for j = 1:length(t)

T(j+1)=T(j)+dt;

M1(j+1)=M1(j)+1./(1+exp(-T(j)));

M1(j+1) = M1(j)+(dt*(delta*M1(j+1)*(1-(M1(j+1)/gamma))-2*K1*M1(j+1)*M1(j+1)-M1(j+1)*sumter(j+1))-((Oa-n)*K6*M1(j+1)*M6(j+1))-((Pa-Oa)*Ko*M1(j+1)*O(j+1))-(mu_1*M1(j+1)));

M2(j+1) = M2(j)+(dt*(K1*M1(j)*M1(j)-K2*M1(j)*M2(j))-(mu_2*M2(j+1))-dtmakro1+dtmakro2);

M3(j+1) = M3(j)+(dt*(K2*M1(j)*M2(j)-K3*M1(j)*M3(j))-mu_3*M3(j));

M4(j+1) = M4(j)+(dt*(K3*M1(j)*M3(j)-K4*M1(j)*M4(j))-mu_4*M4(j));

M5(j+1) = M5(j)+(dt*(K4*M1(j)*M4(j)-K5*M1(j)*M5(j))-mu_5*M5(j));

M6(j+1) = M6(j)+(dt*(K5*M1(j)*M5(j)-K6*M1(j)*M6(j))-mu_6*M6(j));

O(j+1) = O(j)+(dt*(K6*M1(j)*M6(j)-Ko*M1(j)*O(j)-mu_o*O(j)));

P(j+1) = P(j)+(dt*(Ko*M1(j)*O(j)-mu_p*P(j)));

M12(j+1) = M12(j)+(dt*(delta*M12(j+1)*(1-(M12(j+1)/gamma))-2*K1*M12(j+1)*M12(j+1)-M12(j+1)*sumter(j+1))-((Oa-n)*K6*M12(j+1)*M62(j+1))-((Pa-Oa)*Ko*M12(j+1)*O2(j+1))-(mu_1*M12(j+1)));

M22(j+1) = M22(j)+(dt*(K1*M12(j)*M12(j)-K2*M12(j)*M22(j))-(mu_2*M22(j+1)));

M32(j+1) = M32(j)+(dt*(K2*M12(j)*M22(j)-K3*M12(j)*M32(j))-mu_3*M32(j));

M42(j+1) = M42(j)+(dt*(K3*M12(j)*M32(j)-K4*M12(j)*M42(j))-mu_4*M42(j));

M52(j+1) = M52(j)+(dt*(K4*M12(j)*M42(j)-K5*M12(j)*M52(j))-mu_5*M52(j));

M62(j+1) = M62(j)+(dt*(K5*M12(j)*M52(j)-K6*M12(j)*M62(j))-mu_6*M62(j));

O2(j+1) = O2(j)+(dt*(K6*M12(j)*M62(j)-Ko*M12(j)*O2(j)-mu_o*O2(j)));

P2(j+1) = P2(j)+(dt*(Ko*M12(j)*O2(j)-mu_p*P2(j)));

end

%figure

%subplot (3,1,1)

figure

%hold on

plot(T,M1,'r', T,M12,'b','Linewidth',2)

legend ('M1', 'M12');

xlabel('time (days)')

ylabel('M1 (gr/ml)')

%subplot (3,1,2)

figure

%hold on

plot(T,M2,'b',T,M22,'r','Linewidth',2)

legend ('M2', 'M22');

xlabel('time (days)')

ylabel('M2 (gr/ml)')

%subplot (3,1,3)

figure

%hold on

plot(T,M3,'g',T,M32,'b','Linewidth',2)

legend ('M3', 'M32');

xlabel('time (days)')

ylabel('M3 (gr/ml)')

%subplot (2,1,2)

figure

%hold on

plot(T,M4,'m',T,M42,'g','Linewidth',2)

legend ('M4', 'M42');

xlabel('time (days)')

ylabel('M4 (gr/ml)')

%subplot (2,1,1)

figure

%hold on

plot(T,M5,'k',T,M52,'g','Linewidth',2)

legend ('M5', 'M52');

xlabel('time (days)')

ylabel('M5 (gr/ml)')

figure

%hold on

plot(T,M6,'m',T,M62,'g','Linewidth',2)

legend ('M6', 'M62');

xlabel('time (days)')

ylabel('M6 (gr/ml)')

%subplot (2,1,1)

figure

%hold on

plot(T,O,'k',T,O2,'g','Linewidth',2)

legend ('O', 'O2');

xlabel('time (days)')

ylabel('O (gr/ml)')

figure

%hold on

plot(T,P,'m',T,P2,'g','Linewidth',2)

legend ('P', 'P2');

xlabel('time (days)')

ylabel('P (gr/ml)')

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Answer 1

Steven Lord on 19 Jul 2023

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https://au.mathworks.com/matlabcentral/answers/1998078-can-graphs-in-matlab-provide-a-more-detailed-scale#answer_1275718

Open in MATLAB Online

Let's look at the data you used to create your first graph as I suspect the insights we gain from that will apply to the others as well.

clc;clear;
%input parameter
  delta=50;
  gamma=75;
  K1=10^-4;
  K2=5*10^-4;
  K3=10^-3;
  K4=5*10^-3;
  K5=10^-2;
  K6=5*10^-2;
  Ko=0.1;
  n=6;
  Oa=10;
  Pa=100;
  mu_1=10^-3;
  mu_2=10^-3;
  mu_3=10^-3;
  mu_4=10^-3;
  mu_5=10^-3;
  mu_6=10^-3;
  mu_o=10^-4;
  mu_p= 10^-5;
%komponen paper 1
Mg = 0.047;
lambdaMf = 2*10^-1;
Fo = 3.36*10^-5;
KFo = 2.58*10^-5;
lambdaMa = 2.3*10^-1;
Ao = 0.14;
KAo = 10^-2;
beta = 10;
epsilon1 = 0.3333;
epsilon2 = 0.8;
lambdaM1Tb = 6*10^-1;
Tb=10^-2;
KTb = 2.5*10^-3;
makro1 = 0.02;
makro2 = 0.02;
dM1= 0.015;
dM2= 0.015;
dtmakro1 = (Mg*(lambdaMf*(Fo/(Fo+KFo))+lambdaMa*(Ao/(Ao+KAo)))*(epsilon1*beta/(beta*(epsilon1+epsilon2)))*makro1)+(lambdaM1Tb*(Tb/(Tb+KTb))*makro1)-dM1*makro1;
dtmakro2 = (Mg*(lambdaMf*(Fo/(Fo+KFo))+lambdaMa*(Ao/(Ao+KAo)))*(epsilon2/(beta*(epsilon1+epsilon2)))*makro2)+(lambdaM1Tb*(Tb/(Tb+KTb))*makro1)-dM2*makro2;
%input initial condition
M1(1)=10;
M2(1)=0;
M3(1)=0;
M4(1)=0;
M5(1)=0;
M6(1)=0;
O(1)=0;
P(1)=0;
M12(1)=10;
M22(1)=0;
M32(1)=0;
M42(1)=0;
M52(1)=0;
M62(1)=0;
O2(1)=0;
P2(1)=0;
%input for time
t(1)=0;
dt=0.01; %time interval
t=0:dt:100; %time span
%input empty array
T=zeros(length(t)+1,1); %empty array for t
M1=zeros(length(t)+1,1); %empty array for M1
M2=zeros(length(t)+1,1); %empty array for M2
M3=zeros(length(t)+1,1); %empty array for M3
M4=zeros(length(t)+1,1); %empty array for M4
M5=zeros(length(t)+1,1); %empty array for M5
M6=zeros(length(t)+1,1); %empty array for M6
O=zeros(length(t)+1,1);
P=zeros(length(t)+1,1);
M12=zeros(length(t)+1,1); %empty array for M1
M22=zeros(length(t)+1,1); %empty array for M2
M32=zeros(length(t)+1,1); %empty array for M3
M42=zeros(length(t)+1,1); %empty array for M4
M52=zeros(length(t)+1,1); %empty array for M5
M62=zeros(length(t)+1,1); %empty array for M6
O2=zeros(length(t)+1,1);
P2=zeros(length(t)+1,1);
sumter=K2*M2+K3*M3+K4*M4+K5*M5;
for j = 1:length(t)
    T(j+1)=T(j)+dt;
    M1(j+1)=M1(j)+1./(1+exp(-T(j)));
    M1(j+1) = M1(j)+(dt*(delta*M1(j+1)*(1-(M1(j+1)/gamma))-2*K1*M1(j+1)*M1(j+1)-M1(j+1)*sumter(j+1))-((Oa-n)*K6*M1(j+1)*M6(j+1))-((Pa-Oa)*Ko*M1(j+1)*O(j+1))-(mu_1*M1(j+1)));
    M2(j+1) = M2(j)+(dt*(K1*M1(j)*M1(j)-K2*M1(j)*M2(j))-(mu_2*M2(j+1))-dtmakro1+dtmakro2);
    M3(j+1) = M3(j)+(dt*(K2*M1(j)*M2(j)-K3*M1(j)*M3(j))-mu_3*M3(j));
    M4(j+1) = M4(j)+(dt*(K3*M1(j)*M3(j)-K4*M1(j)*M4(j))-mu_4*M4(j));
    M5(j+1) = M5(j)+(dt*(K4*M1(j)*M4(j)-K5*M1(j)*M5(j))-mu_5*M5(j));
    M6(j+1) = M6(j)+(dt*(K5*M1(j)*M5(j)-K6*M1(j)*M6(j))-mu_6*M6(j));
    O(j+1)  = O(j)+(dt*(K6*M1(j)*M6(j)-Ko*M1(j)*O(j)-mu_o*O(j)));
    P(j+1)  = P(j)+(dt*(Ko*M1(j)*O(j)-mu_p*P(j)));
    M12(j+1) = M12(j)+(dt*(delta*M12(j+1)*(1-(M12(j+1)/gamma))-2*K1*M12(j+1)*M12(j+1)-M12(j+1)*sumter(j+1))-((Oa-n)*K6*M12(j+1)*M62(j+1))-((Pa-Oa)*Ko*M12(j+1)*O2(j+1))-(mu_1*M12(j+1)));
    M22(j+1) = M22(j)+(dt*(K1*M12(j)*M12(j)-K2*M12(j)*M22(j))-(mu_2*M22(j+1)));
    M32(j+1) = M32(j)+(dt*(K2*M12(j)*M22(j)-K3*M12(j)*M32(j))-mu_3*M32(j));
    M42(j+1) = M42(j)+(dt*(K3*M12(j)*M32(j)-K4*M12(j)*M42(j))-mu_4*M42(j));
    M52(j+1) = M52(j)+(dt*(K4*M12(j)*M42(j)-K5*M12(j)*M52(j))-mu_5*M52(j));
    M62(j+1) = M62(j)+(dt*(K5*M12(j)*M52(j)-K6*M12(j)*M62(j))-mu_6*M62(j));
    O2(j+1)  = O2(j)+(dt*(K6*M12(j)*M62(j)-Ko*M12(j)*O2(j)-mu_o*O2(j)));
    P2(j+1)  = P2(j)+(dt*(Ko*M12(j)*O2(j)-mu_p*P2(j)));
end

Let's plot M1 and M12 separately.

figure

plot(T, M1)

figure

plot(T, M12)

Are the values in M12 actually exactly 0?

[minvalue, maxvalue] = bounds(M12)
minvalue = 0
maxvalue = 0

So that looks correct. How about the other variables whose names end with 2?

[minvalue, maxvalue] = bounds(M22)
minvalue = 0
maxvalue = 0
[minvalue, maxvalue] = bounds(M32)
minvalue = 0
maxvalue = 0
[minvalue, maxvalue] = bounds(M42)
minvalue = 0
maxvalue = 0
[minvalue, maxvalue] = bounds(M52)
minvalue = 0
maxvalue = 0
[minvalue, maxvalue] = bounds(M62)
minvalue = 0
maxvalue = 0
[minvalue, maxvalue] = bounds(O2)
minvalue = 0
maxvalue = 0
[minvalue, maxvalue] = bounds(P2)
minvalue = 0
maxvalue = 0

Given the data, all those plots you showed look correct to me. Could you say more about what you believe MATLAB is doing / plotting incorrectly?

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