How change design of my graph to this shape by data analysis?
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0 votes
i want my graph be like that like dimension and color ?
and i can plot the same plot for 2D like second graph too?
data = load('ST3.txt');
core = reshape(data(:, 3),100,100).';
figure()
surf(real(core),'edgecolor', 'none')
xlabel('x'); ylabel('y'); zlabel('ST1'); title('real ST1');
figure()
surf(imag(core),'edgecolor', 'none')
xlabel('x'); ylabel('y'); zlabel('ST1'); title('imag ST1');
figure()
surf(abs(core),'edgecolor', 'none')
xlabel('x'); ylabel('y'); zlabel('ST1'); title('abs ST1');
Accepted Answer
Star Strider
on 25 Dec 2024
I am not certain what you are asking.
Choosing a matching colormap is straightforward, and turbo seems to be close to that in your posted image.
With respect to thd 2D plots, the patch documentation offers an approach that I copied and adapted here.
Examples —
data = load('ST3.txt');
core = reshape(data(:, 3),100,100).';
figure()
surf(real(core),'edgecolor', 'none')
xlabel('x'); ylabel('y'); zlabel('ST1'); title('real ST1');
colormap(turbo)
figure()
surf(imag(core),'edgecolor', 'none')
xlabel('x'); ylabel('y'); zlabel('ST1'); title('imag ST1');
●
figure()
surf(abs(core),'edgecolor', 'none')
xlabel('x'); ylabel('y'); zlabel('ST1'); title('abs ST1');
colormap(turbo)
figure()
surf(abs(core),'edgecolor', 'none')
xlabel('x'); ylabel('y'); zlabel('ST1'); title('abs ST1 (Top View)');
colormap(turbo)
view(0,90)
xv = linspace(0, 1, size(core,1));
Choose_Row = 50;
yv = core(Choose_Row,:); % Choose The Rows Or Column You Want To Plot
yv(end) = NaN;
figure
patch(xv, yv, yv, EdgeColor='interp', LineWidth=2)
grid
colormap(turbo)
xlabel('x')
ylabel('y')
title("Row "+Choose_Row)
.
9 Comments
@Star Strider thank for your code can i plot 2D for 3 or 4 ST3.txt and show the effect of all in one plot?and why we don't have contour and why the noies effect not shown in 2D like this
salim
on 25 Dec 2024
@Star Strider if possible i need the range of x-axis becuase the graph is not clear to much i want play with range untill i get a good vision where should i add ?
Star Strider
on 26 Dec 2024
Edited: Star Strider
on 26 Dec 2024
It is difficult to work with your data, particularly in extracting the complex data from the third column of your text files. That is stored in a non-standard notation, and it took a while for me to figure out how best to extract it.
I came up with this to run on MATLAB Online —
% files = dir('*.txt')
files = {"https://www.mathworks.com/matlabcentral/answers/uploaded_files/1821985/ST1.txt"; "https://www.mathworks.com/matlabcentral/answers/uploaded_files/1821986/ST2.txt"; "https://www.mathworks.com/matlabcentral/answers/uploaded_files/1821987/ST3.txt"}
files = 3x1 cell array
{["https://www.mathworks.com/matlabcentral/answers/uploaded_files/1821985/ST1.txt"]}
{["https://www.mathworks.com/matlabcentral/answers/uploaded_files/1821986/ST2.txt"]}
{["https://www.mathworks.com/matlabcentral/answers/uploaded_files/1821987/ST3.txt"]}
for k = 1:numel(files)
start = strfind(files{k},'ST');
filec = char(files{k});
filename = filec(start:numel(char(files{k})))
file{k} = websave(filename,filec);
end
filename = 'ST1.txt'
filename = 'ST2.txt'
filename = 'ST3.txt'
tic
for k1 = 1:numel(files)
% filename = files(k).name
filename = file{k1}
STT{k1,:} = readtable(filename);
Var1 = STT{k1}{:,1};
Var2 = STT{k1}{:,2};
% disp(ST{k})
STT{k1}{:,3} = replace(STT{k1}{:,3}, {'.*I','*I'},{'i','i'});
STT{k1}{:,3} = replace(STT{k1}{:,3}, {'.','-.','+.'},{' .',' -.',' +.'});
Col3 = cellfun(@(x)sscanf(x,'%f%f'),STT{k1}{:,3}, 'Unif',0);
ne0 = cellfun(@(x)all((x ~= 0),'all'), Col3, 'Unif',0);
Col3c = zeros(size(STT{k1},1),1);
for k2 = 1:numel(Col3)
if (numel(Col3{k2}) == 2) & ne0{k2}
% C3k = Col3{k}
Col3c(k2) = [1 1i]*Col3{k2};
elseif (numel(Col3{k2}) == 3) & ne0{k2}
Col3c(k2) = [1 1 1i]*Col3{k2};
end
end
% filename
size(STT{k1})
fsp = filesep;
sep = strfind(filename,fsp);
pfx = filename(sep(end)+(1:3))
Var3 = Col3c;
STOut = table(Var1,Var2,Var3);
matfile = string(pfx)+".mat"
save(matfile, 'STOut')
toc
STTT{k1} = STOut;
disp(STOut)
end
filename = '/users/mss.system.hgtc6/ST1.txt'
ans = 1×2
10201 3
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
pfx = 'ST1'
matfile = "ST1.mat"
Elapsed time is 0.373310 seconds.
Var1 Var2 Var3
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27 ...
filename = '/users/mss.system.hgtc6/ST2.txt'
ans = 1×2
10201 3
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
pfx = 'ST2'
matfile = "ST2.mat"
Elapsed time is 1.952913 seconds.
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27 ...
filename = '/users/mss.system.hgtc6/ST3.txt'
ans = 1×2
10201 3
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
pfx = 'ST3'
matfile = "ST3.mat"
Elapsed time is 4.476246 seconds.
Var1 Var2 Var3
____ ____ _______________________
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25 12 0.63582+0.95252i
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25 64 0.56356+0.92875i
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26 2 -0.6809+0.83105i
26 3 -0.5765+0.90656i
26 4 -0.46381+0.96902i
26 5 0.65554+0.017537i
26 6 0.77983+0.051414i
26 7 0.90729+0.070163i
26 8 1.0361+0.073515i
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26 11 0.41186+0.99183i
26 12 0.5276+0.93533i
26 13 0.63573+0.86539i
26 14 0.7347+0.783i
26 15 0.82308+0.68936i
26 16 0.89961+0.58582i
26 17 0.96317+0.47386i
26 18 1.0129+0.35512i
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26 20 1.068+0.10415i
26 21 1.0726-0.024447i
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26 23 1.0358-0.27863i
26 24 0.99481-0.40056i
26 25 0.93953-0.51667i
26 26 0.87075-0.62531i
26 27 0.78946-0.7249i
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26 30 0.48313-0.95582i
26 31 -0.63487+0.0064889i
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26 35 -1.1407+0.059844i
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26 37 -0.38825-0.99512i
26 38 -0.50433-0.94101i
26 39 -0.61302-0.87339i
26 40 -0.71275-0.79326i
26 41 -0.80207-0.70178i
26 42 -0.87971-0.6003i
26 43 -0.94454-0.4903i
26 44 -0.99564-0.3734i
26 45 -0.96772-0.25131i
26 46 -0.94604-0.12581i
26 47 -0.93964+0.0012666i
26 48 -0.94856+0.12806i
26 49 -0.97267+0.25271i
26 50 -0.98844+0.3734i
26 51 -0.93535+0.48837i
26 52 -0.86888+0.59596i
26 53 -0.79003+0.69458i
26 54 -0.7+0.78282i
26 55 -0.60015+0.85941i
26 56 -0.49198+0.92325i
26 57 -0.37712+0.97344i
26 58 0.74272+0.0092847i
26 59 0.86572+0.030304i
26 60 0.99005+0.03625i
26 61 1.1139+0.027099i
26 62 1.2353+0.0030606i
26 63 0.35253+0.96457i
26 64 0.46387+0.91229i
26 65 0.56765+0.84708i
26 66 0.66238+0.77001i
26 67 0.74667+0.68232i
26 68 0.81932+0.58542i
26 69 0.87934+0.48084i
26 70 0.92591+0.37025i
26 71 0.95845+0.25538i
26 72 0.97659+0.13802i
26 73 0.98021+0.019997i
26 74 0.96941-0.096875i
26 75 0.94451-0.21082i
26 76 0.90609-0.32011i
26 77 0.8549-0.42313i
26 78 0.79191-0.51838i
26 79 0.71827-0.6045i
26 80 0.63528-0.6803i
26 81 0.54438-0.74476i
26 82 0.44711-0.79709i
26 83 0.3451-0.83669i
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26 101 -0.69875+0.1402i
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27 2 -0.90342+0.94783i
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27 5 0.48843+0.20521i
27 6 0.6364+0.25774i
27 7 0.78959+0.29217i
27 8 0.9458+0.30801i
27 9 1.1028+0.30503i
27 10 1.2582+0.28328i
27 11 1.41+0.24307i
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27 27 1.0259-0.80909i
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27 43 -0.89583-0.68277i
27 44 -0.8229-0.54528i
27 45 -0.76706-0.40013i
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27 48 -0.70871+0.059693i
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27 50 -0.76266+0.36412i
27 51 -0.81662+0.50899i
27 52 -0.88755+0.64613i
27 53 -0.97438+0.77354i
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27 58 0.59398+0.20658i
27 59 0.74192+0.24373i
27 60 0.89292+0.26265i
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27 62 1.1951+0.24526i
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27 64 1.4825+0.15612i
27 65 1.6154+0.086362i
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27 67 0.84962+0.90219i
27 68 0.94762+0.79074i
27 69 1.031+0.66869i
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27 91 -0.48471-0.90046i
27 92 -0.58155-0.82537i
27 93 -0.66736-0.74004i
27 94 -0.7411-0.64606i
27 95 -0.80195-0.54511i
27 96 -0.84932-0.43892i
27 97 -0.88288-0.32929i
27 98 -0.9025-0.21798i
27 99 -0.90828-0.10675i
27 ...
This actually works and produces appropriate results (the output matches the input files).
Note that using load will not properly load your data, since it ignores the imaginary part of the complex data. This code reads it correctly.
It has taken me most of the day to figure out how to read your file. I’m stopping here for the time being. I will return to see if I can produce the plots you want tomorrow. End of my day here.
files = dir('*.txt')
and then in the loop:
filename = files(k).name
to import each one. I use readtable here because otherwise the imaginary part of the complex data in the third column is not imported.
EDIT — (26 Dec 2024 at 03:27)
I decided to finish this up tonight.
for k = 1:numel(STTT)
[U1,idx1] = unique(STTT{k}{:,1});
didx1 = diff(idx1);
[x1,x2] = bounds(didx1)
X{k} = reshape(STTT{k}{:,1},x1,[]);
Y{k} = reshape(STTT{k}{:,2},x1,[]);
Z{k} = reshape(STTT{k}{:,3},x1,[]);
end
x1 = 101
x2 = 101
x1 = 101
x2 = 101
x1 = 101
x2 = 101
for k = 1:numel(Z)
figure
surfc(X{k}, Y{k}, abs(Z{k}))
grid on
colormap(turbo)
colorbar
xlabel('x')
ylabel('y')
zlabel('|z|')
title("ST"+k+" Surface & Contour Plot")
figure
surf(X{k}, Y{k}, abs(Z{k}), EdgeColor='interp')
grid on
colormap(turbo)
colorbar
view(0,90)
axis('tight')
xlabel('x')
ylabel('y')
zlabel('|z|')
title("ST"+k+" Surface Plot (Top View)")
figure
surf(X{k}, Y{k}, abs(Z{k}), EdgeColor='interp')
grid on
colormap(turbo)
colorbar
view(90,0)
axis('tight')
xlabel('x')
ylabel('y')
zlabel('|z|')
title("ST"+k+" Surface Plot (Y Side View)")
figure
surf(X{k}, Y{k}, abs(Z{k}), EdgeColor='interp')
grid on
colormap(turbo)
colorbar
view(180,0)
axis('tight')
xlabel('x')
ylabel('y')
zlabel('|z|')
title("ST"+k+" Surface Plot (X Side View)")
figure
hp11 = plot(X{k}, real(Z{k}), '-r', DisplayName='Re');
hold on
hp12 = plot(X{k}, imag(Z{k}), '-b', DisplayName='Im');
hp13 = plot(X{k}, abs(Z{k}), '-k', DisplayName='|Z|');
hold off
grid on
xlabel('x')
zlabel('z')
title("ST"+k+" 2D X-Z Plot")
legend([hp11(1) hp12(1) hp13(1)], Location='best')
figure
hp21 = plot(Y{k}, real(Z{k}), '-r', DisplayName='Re');
hold on
hp22 = plot(Y{k}, imag(Z{k}), '-b', DisplayName='Im');
hp23 = plot(Y{k}, abs(Z{k}), '-k', DisplayName='|Z|');
hold off
grid on
xlabel('y')
zlabel('z')
title("ST"+k+" 2D Y-Z Plot")
legend([hp21(1) hp22(1) hp23(1)], Location='best')
end
This is the best I can do with these.
I did not do the colour gradient 2D line, because I do not know what row or column of ‘Z’ you want to plot. You can easily adapt that code to the row or column you want to plot.
.
salim
on 26 Dec 2024
@Star Strider if you watcch this maybe figure out
the shape of graph is same but without noise 
the left graph and your graph in right are same so he use matlab too, and he must use data too, becuase winer process i don't know how work i have program maybe help i will put here too
% Parameters
n = 1;
A = 1; % Amplitude
B = 1; % Width parameter of soliton
v = 1; % Velocity of soliton
alpha = 1; % Nonlinearity parameter
beta = 0.1; % Dispersion parameter
gamma = 0.05; % Noise intensity for multiplicative noise
% Grid for xi and time
xi = linspace(-20, 20, 2000); % Spatial range in transformed coordinates
t = linspace(-20, 20, 2000); % Time range
dt = t(2) - t(1); % Time step
% Generate Wiener process (increments) for time
W = cumsum(sqrt(dt) * randn(size(t))); % Wiener process for each time step
W = gamma * W; % Scale Wiener process by gamma
% Define initial soliton profile (deterministic solution)
u_deterministic = abs(((tanh(0.192e3 ./ 0.35e2 .* t' + xi) - 0.1e1) .^ (0.1e1 ./ 0.4e1)) .* exp(i * (-0.3e1 .* xi - 0.8e1 * t' + (3 .* W' ))));
% Ensure u_deterministic has the correct dimensions (time x space)
if size(u_deterministic, 1) ~= length(t) || size(u_deterministic, 2) ~= length(xi)
error('Mismatch in dimensions of u_deterministic. Check calculations.');
end
% Initialize storage for the stochastic solution
u_stochastic = zeros(length(t), length(xi));
noise=zeros(size(u_stochastic));
% Time evolution
for n = 1:length(t)
% Apply the Wiener noise factor at time step n
noise_factor = 1 + W(n);
noise(n,:)=noise_factor;
% Update solution with multiplicative noise
u_stochastic(n, :) = u_deterministic(n, :) * noise_factor;
end
% Plot the 3D surface of the stochastic Kudryashov soliton
figure;
surfc(xi, t, u_deterministic, 'EdgeColor', 'none');
title('Deterministic (Wiener Process)');
xlabel('Transformed Space (\xi)');
ylabel('Time (t)');
zlabel('Amplitude (u)');
colormap jet;
figure
surfc(xi, t, noise, 'EdgeColor', 'none');
title('Multiplicative Noise (Wiener Process)');
xlabel('Transformed Space (\xi)');
ylabel('Time (t)');
zlabel('Amplitude (u)');
colormap jet;
figure
surfc(xi, t, u_stochastic, 'EdgeColor', 'none');
title('Stochastic (Wiener Process)');
xlabel('Transformed Space (\xi)');
ylabel('Time (t)');
zlabel('Amplitude (u)');
colormap jet;
figure
surfc(xi, t, u_stochastic, 'EdgeColor', 'none');
title('Stochastic (Wiener Process)');
xlabel('Transformed Space (\xi)');
ylabel('Time (t)');
zlabel('Amplitude (u)');
colormap jet;
xlim([0 20])
ylim([-5 1])
zlim([-1 2])
Star Strider
on 26 Dec 2024
How does this relate to the ‘ST’ files you proviided? They appear to be entirely unrelated.
Also, none of the ‘ST’ files you provided when plotted look remotely like the plots you describe in your last Comment.
What exactly are you doing?
salim
on 27 Dec 2024
@Star Strider they are totally related, maybe ask this how? i plot this function Q3 and other function like this 
i solved this problem with maple it depend of function solution also you can change for (sinh or tanh) is depend on what solution we get at maple, i give the parameter number it is arbitrary except W(t) and x&t are range so W(t) is wiener process make a matrix and my friend which is expert at maple combined them and we got ploted the function and all noise is apear at there but for publishing paper is not suitable becuase of that i want to plot in matlab as in picture
this is maple progress which information of matrix i export to get plot but i don't know why is not like the graph of paper How he get that plot with effect of noise i am looking for that
Star Strider
on 27 Dec 2024
I cannot run your Maple code (I probably should get Maple again) even if was in a form that I could copy and paste. (I cannot run images of code.)
My question is simply: how does that relate to the data in the files you posted? Are you attenpting to do some sort of transformation of the file data to create the synthesised data? If so, what is that transformation?
I still do not understand what you want to do with the data in the files, especially with respect to the Maple or MATLAB code. (All I did was to read them and plot the results, with various views of the plotted surfaces.)
salim
on 27 Dec 2024
@Star Strider undrestanding is not easy but i am beliave that if you undrestand you can plot this is best shap i will upload code of maple here also if you watch my function contain W(t) function which is wainer process (brawnian motion) in stochastic is random variable which for make a function have a niose , in a lot of paper of solving NPDE have this W(t) so i have to plot the function which contain random variable if you watch STX matrix in picture you will see TX_Wfree *~ T_W which is combine function with W(t) function and make effect is shown up
code of maple:
restart;
currentdir(kernelopts(':-homedir')):
randomize():
local gamma:
T3 := (B[1]*(tanh(2*n^2*(delta^2 - psi)*k*t/((k*n - 1)*(k*n + 1)) + x) - 1))^(1/(2*n))*exp((-k*x + psi*t + delta*W(t) - delta^2*t)*I);
/ 1 \
|---|
\2 n/
/ / / 2 / 2 \ \ \\
| | |2 n \delta - psi/ k t | || / /
T3 := |B[1] |tanh|----------------------- + x| - 1|| exp\I \
\ \ \ (k n - 1) (k n + 1) / //
2 \\
-k x + psi t + delta W(t) - delta t//
params := {B[1]=1, n=2, delta=0.5, psi=1, k=3 }:
insert numerical values
solnum :=subs(params, T3):
N := 100:
use Finance in:
Wiener := WienerProcess():
P := PathPlot(Wiener(t), t = 0..10, timesteps = N, replications = 1):
end use:
W__points := plottools:-getdata(P)[1, -1];
t_grid := convert(W__points[..,1], list):
x_grid := [seq(-2..2, 4/N)]:
T3_Wfree := eval(T3, {W(t)=0, op(params)});
TX_Wfree := Matrix((N+1)$2, (it, ix) -> evalf(eval(T3_Wfree, {t=t_grid[it], x=x_grid[ix]}))):
T_W := Matrix((N+1)$2, (it, ix) -> W__points[it, 2]):
STX := TX_Wfree *~ T_W;
plots:-matrixplot(Re~(STX));plots:-matrixplot(abs~(STX));plots:-matrixplot(Im~(STX))
MatlabFile := "ST3.txt";
ExportMatrix(MatlabFile, STX, target = MATLAB, format = rectangular, mode = ascii, format = entries);
427884
currentdir();
Star Strider
on 27 Dec 2024
I am sorry, however I still do not understand what you want to do. I now get the impression that this involves some sort of partial differential equation, however the relation of that and the posted files is still completely unclear.
So far, none of this makes any sense to me. I do not understand how the posted files figure into what you want to do.
You will need to explain what you want to do clearly, completely, and concisely, as well as how the posted files figure into it. I understand that putting complicated mathematical concepts into words can be difficult, however you need to take the time to do that.
.
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