plot some nodes from an .txt file

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Alberto Acri 2022-12-1

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https://ww2.mathworks.cn/matlabcentral/answers/1868443-plot-some-nodes-from-an-txt-file

评论： Mathieu NOE 2024-6-12

采纳的回答： Mathieu NOE

filename.txt

Hi! I would like to hope if there is an easy way to plot only the outermost nodes (the red nodes in the figure).

I am leaving the filename.txt file representing the nodes.

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Answer 1

Mathieu NOE 2022-12-1

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hello Alberto

here you are ; use function boundary with shrink factor = 1

data = readmatrix('filename.txt');
x = data(:,1);
y = data(:,2);
% k = boundary(___,s) specifies shrink factor s using any of the previous syntaxes.
% s is a scalar between 0 and 1. Setting s to 0 gives the convex hull,
% and setting s to 1 gives a compact boundary that envelops the points.
% The default shrink factor is 0.5.
s = 1;
k = boundary(x,y,s);
plot(x,y, 'db', x(k), y(k), '-r')

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Mathieu NOE 2022-12-1

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hello again

here there is one "stupid" solution as we can see the two groups are separetd by a blank zone in the y range around y = 230

so you can tell which group of data we are dealing with depending of if it's above or below that line.

for more complex cases with multiple curves you may have to use more sophisticated tools like clustering or based on Image Processing like here :

detect closed shapes formed by several arrays of points - MATLAB Answers - MATLAB Central (mathworks.com)

"stupid" code for the time being :

% data = readmatrix('filename.txt');
data = readmatrix('filename_1.txt');
x = data(:,1);
y = data(:,2);
% k = boundary(___,s) specifies shrink factor s using any of the previous syntaxes.
% s is a scalar between 0 and 1. Setting s to 0 gives the convex hull,
% and setting s to 1 gives a compact boundary that envelops the points.
% The default shrink factor is 0.5.
s = 1;
%upper curve
idx = y>235;
x_temp = x(idx);
y_temp = y(idx);
k = boundary(x_temp,y_temp,s);
x_out1 = x_temp(k);
y_out1 = y_temp(k);
%lower curve
idx = y<=235;
x_temp = x(idx);
y_temp = y(idx);
k = boundary(x_temp,y_temp,s);
x_out2 = x_temp(k);
y_out2 = y_temp(k);
plot(x,y, 'db', x_out1, y_out1, '*r', x_out2, y_out2, '*c')

Mathieu NOE 2022-12-1

在 MATLAB Online 中打开

a slight improvement, we compute the y threshold value , but that works only if the two curves have a vertical separation > 0

% data = readmatrix('filename.txt');
data = readmatrix('filename_1.txt');
x = data(:,1);
y = data(:,2);
% k = boundary(___,s) specifies shrink factor s using any of the previous syntaxes.
% s is a scalar between 0 and 1. Setting s to 0 gives the convex hull,
% and setting s to 1 gives a compact boundary that envelops the points.
% The default shrink factor is 0.5.
s = 1;
% find the y threshold that separates the two curves 
% NB : it works only if there is a sufficient gap in the y direction (here
% we take 5 units separation in y distance
yy = unique(sort(y));
ind = find(diff(yy)>5);
val_low = yy(ind);
val_upper = yy(ind+1);
threshold = (val_low+val_upper)/2; % let's take the average of the two
%upper curve
idx = y>threshold;
x_temp = x(idx);
y_temp = y(idx);
k = boundary(x_temp,y_temp,s);
x_out1 = x_temp(k);
y_out1 = y_temp(k);
%lower curve
idx = y<threshold;
x_temp = x(idx);
y_temp = y(idx);
k = boundary(x_temp,y_temp,s);
x_out2 = x_temp(k);
y_out2 = y_temp(k);
plot(x,y, 'db', x_out1, y_out1, '*r', x_out2, y_out2, '*c')

Alberto Acri 2022-12-2

编辑：Alberto Acri 2022-12-2

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filename_3.txt

I will take your solution into consideration.

In the case where I have 4 curves, as in the 'filename_3.txt' case, how could I get the same results as above?

I tried with this code but there is some problem on identifying the parameters 'val_low' and 'val_upper'.

data = readmatrix('filename_3.txt');
x = data(:,1);
y = data(:,2);
s = 1;
gap = 1;
xx = unique(sort(x));
ind_x = find(diff(xx)>gap);
val_low_x = xx(ind_x);
val_upper_x = xx(ind_x + 1);
threshold_x = (val_low_x + val_upper_x)/2; % let's take the average of the two
% upper curve
idx_x = x > threshold_x;
x_temp_x = x(idx_x);
y_temp_x = y(idx_x);
k_x = boundary(x_temp_x,y_temp_x,s);
x_out1 = x_temp_x(k_x);
y_out1 = y_temp_x(k_x);
% lower curve
idx_x = x < threshold_x;
x_temp_x = x(idx_x);
y_temp_x = y(idx_x);
k_x = boundary(x_temp_x,y_temp_x,s);
x_out2 = x_temp_x(k_x);
y_out2 = y_temp_x(k_x);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
yy = unique(sort(y));
ind_y = find(diff(yy) > gap);
val_low_y = yy(ind_y);
val_upper_y = yy(ind_y + 1);
threshold_y = (val_low_y + val_upper_y)/2; % let's take the average of the two
% upper curve
idx_y = y > threshold_y;
x_temp_y = x(idx_y);
y_temp_y = y(idx_y);
k_y = boundary(x_temp_y,y_temp_y,s);
x_out3 = x_temp_y(k_y);
y_out3 = y_temp_y(k_y);
% lower curve
idx_y = y < threshold_y;
x_temp_y = x(idx_y);
y_temp_y = y(idx_y);
k_y = boundary(x_temp_y,y_temp_y,s);
x_out4 = x_temp_y(k_y);
y_out4 = y_temp_y(k_y);
plot(x,y, 'db', x_out1, y_out1, '*r', x_out2, y_out2, '*r', x_out3, y_out3, '*r', x_out4, y_out4, '*r')

So I thought of separating the 4 curves into 4 quadrants with 4 for loops, but I don't have many ideas.

data = readmatrix('filename_3.txt');
x = data(:,1);
y = data(:,2);
s = 1;
% X-axis separation
X = 350; 
% Y-axis separation
Y = 240;
% Upper left quadrant
for x<X & y>Y
    
end

Mathieu NOE 2022-12-2

在 MATLAB Online 中打开

This is a new code for this case

maybe with a little more work we can detect how many distinct / non overlapping curves (with always the constrain that x and y gaps > 0)

this is just a first try :

% data = readmatrix('filename.txt');
data = readmatrix('filename_3.txt');
x = data(:,1);
y = data(:,2);
% k = boundary(___,s) specifies shrink factor s using any of the previous syntaxes.
% s is a scalar between 0 and 1. Setting s to 0 gives the convex hull,
% and setting s to 1 gives a compact boundary that envelops the points.
% The default shrink factor is 0.5.
s = 1;
% find the y threshold that separates the two curves 
% NB : it works only if there is a sufficient gap in the y direction (here
% we take 5 units separation in y distance
yy = unique(sort(y));
ind = find(diff(yy)>5);
val_low = yy(ind);
val_upper = yy(ind+1);
y_threshold = (val_low+val_upper)/2; % let's take the average of the two
% same approach to find x gap between left / right 
xx = unique(sort(x));
ind = find(diff(xx)>5);
val_low = yy(ind);
val_upper = yy(ind+1);
x_threshold = (val_low+val_upper)/2; % let's take the average of the two
%upper left curve
idx = x<x_threshold;
idy = y>y_threshold;
x_temp = x(idx & idy);
y_temp = y(idx & idy);
k = boundary(x_temp,y_temp,s);
x_out1 = x_temp(k);
y_out1 = y_temp(k);
%upper right curve
idx = x>x_threshold;
idy = y>y_threshold;
x_temp = x(idx & idy);
y_temp = y(idx & idy);
k = boundary(x_temp,y_temp,s);
x_out2 = x_temp(k);
y_out2 = y_temp(k);
%lower left curve
idx = x<x_threshold;
idy = y<y_threshold;
x_temp = x(idx & idy);
y_temp = y(idx & idy);
k = boundary(x_temp,y_temp,s);
x_out3 = x_temp(k);
y_out3 = y_temp(k);
%lower right curve
idx = x>x_threshold;
idy = y<y_threshold;
x_temp = x(idx & idy);
y_temp = y(idx & idy);
k = boundary(x_temp,y_temp,s);
x_out4 = x_temp(k);
y_out4 = y_temp(k);
plot(x,y, 'db', x_out1, y_out1, '*r', x_out2, y_out2, '*c', x_out3, y_out3, '*g', x_out4, y_out4, '*k')

Mathieu NOE 2022-12-8

在 MATLAB Online 中打开

FYI

attached are some results / comparison between boundary and myboudary for various txt files

NB that myboudary does not require any tweaking (like the shrink factor in boundary

clc
clearvars
close all
data = readmatrix('filename.txt');
% data = readmatrix('test_9.txt');
% data = readmatrix('test_222.txt');
x = data(:,1);
y = data(:,2);
%% solution with matlab "boundary"
% you have to tweak the shrink factor to get the right result (maybe)
% k = boundary(___,s) specifies shrink factor s using any of the previous syntaxes.
% s is a scalar between 0 and 1. Setting s to 0 gives the convex hull,
% and setting s to 1 gives a compact boundary that envelops the points.
% The default shrink factor is 0.5.
s = 0.9;
k = boundary(x,y,s);
x_out = x(k);
y_out = y(k);
figure(1),
subplot(1,2,1),plot(x,y, 'db', x_out, y_out, '*r')
title(['with matlab "boundary", s = ' num2str(s)])
%% solution based on "tight boundary" but applied in both X and Y directions
%https://fr.mathworks.com/matlabcentral/answers/299796-tight-boundary-around-a-set-of-points
[x_selec,y_selec] = myboundary(x,y);
subplot(1,2,2),plot(x, y,'db',x_selec, y_selec,'*r')
title('with "myboundary"')

Mathieu NOE 2022-12-12

编辑：Mathieu NOE 2022-12-12

在 MATLAB Online 中打开

Clustering.zip

hello Alberto

I did another trial based on this FEX :

Boundary extraction (identification and tracing) from point cloud data - File Exchange - MATLAB Central (mathworks.com)

and comparison of the already 2 previous solutions

this is the new code with 3 approaches :

plot for test_2.txt

code (see test3solutions.m in zip attached)

% test txt files : 
% filename.txt                  
% filename_1.txt                
% filename_2.txt                
% filename_3.txt                
% filename_4.txt                
% test_222.txt                  
% test_9.txt
% test_1.txt                    
% test_2.txt                    
data = readmatrix('test_9.txt');
x = data(:,1);
y = data(:,2);
%% test 1 : with DB cluster and  boundary (matlab native function)
shrink_factor = 1;
epsilon = 5;
MinPts = 2;
IDX=DBSCAN(data,epsilon,MinPts);
[x_out1,y_out1] = PlotClusterinResult_mod(data, IDX, shrink_factor); % myboundary
%% test 2 : withDB cluster and   myboundary (my previous suggestion)
epsilon = 5;
MinPts = 2;
out = PlotClusterinResult_mod2(data, IDX); % myboundary
%% test 3 : with find_delaunay_boundary03_fig1 
% (from Fex : https://fr.mathworks.com/matlabcentral/fileexchange/60690-boundary-extraction-identification-and-tracing-from-point-cloud-data?s_tid=ta_fx_results ) 
Fd = 1.5; %Fd = dmax (max point to point distance)
[bids, E, Ne] = find_delaunay_boundary03_fig1(data,Fd); 
%% plot
figure
subplot(1,3,1),plot(x,y, 'db', x_out1, y_out1, '*r')
title(['DB cluster w/ boundary, s = ' num2str(shrink_factor)]);
subplot(1,3,2),plot(x,y, 'db', out(:,1),out(:,2),'*r')
title('DB cluster w/ myboundary');
subplot(1,3,3),plot(x,y, 'db'),hold on
title('find-delaunay-boundary03-fig1');
for ck = 1:numel(bids)
    plot(x(bids{ck}), y(bids{ck}), '*r')
end

Mathieu NOE 2022-12-13

在 MATLAB Online 中打开

hello Alberto

answers are in the code below

yes we can combine the two last options to get best of both solutions

hope it helps

clc
clearvars
close all
% test txt files : 
% filename.txt                  
% filename_1.txt                
% filename_2.txt                
% filename_3.txt                
% filename_4.txt                
% test_222.txt                  
% test_9.txt
% test_1.txt                    
% test_2.txt                    
data = readmatrix('test_1.txt');
x = data(:,1);
y = data(:,2);
%% test 2 : with DB cluster and   myboundary (my previous suggestion)
epsilon = 5;
MinPts = 2;
IDX=DBSCAN(data,epsilon,MinPts);
out = PlotClusterinResult_mod2(data, IDX); % myboundary
%% test 3 : with find_delaunay_boundary03_fig1 
% (from Fex : https://fr.mathworks.com/matlabcentral/fileexchange/60690-boundary-extraction-identification-and-tracing-from-point-cloud-data?s_tid=ta_fx_results ) 
Fd = 1.5; %Fd = dmax (max point to point distance)
[bids, E, Ne] = find_delaunay_boundary03_fig1(data,Fd); 
x3 = [];
y3 = [];
for ck = 1:numel(bids)
    x3 = [x3; x(bids{ck})];
    y3 = [y3; y(bids{ck})];
end
%% combine both datasets and keep unique pairs
out_both = [out; [x3 y3]];
out_both = unique(out_both, 'rows');
%% plot 1 (zoom)
figure
subplot(1,3,1),plot(x,y, 'db', out(:,1),out(:,2),'*r')
xlim([100 160])
ylim([240 280])
title('DB cluster w/ myboundary');
subplot(1,3,2),plot(x,y, 'db', x3,y3,'*r')
xlim([100 160])
ylim([240 280])
title('find-delaunay-boundary03-fig1');
subplot(1,3,3),plot(x,y,'db', out_both(:,1),out_both(:,2),'*r')
xlim([100 160])
ylim([240 280])
title('combined sets');
%% plot 2 (full range)
figure
subplot(1,3,1),plot(x,y, 'db', out(:,1),out(:,2),'*r')
title('DB cluster w/ myboundary');
subplot(1,3,2),plot(x,y, 'db', x3,y3,'*r')
title('find-delaunay-boundary03-fig1');
subplot(1,3,3),plot(x,y,'db', out_both(:,1),out_both(:,2),'*r')
title('combined sets');

Alberto Acri 2023-1-4

Hi @Mathieu NOE! Can I ask you again for your help? Using the attached files (the files you had attached previously), I noticed a "problem" with the file test_114_check.txt of which I also attach an image.

How can I do to extrapolate, even for this curve, only the outer coordinates?

I thank you for your help in advance!

Mathieu NOE 2023-1-4

编辑：Mathieu NOE 2023-1-4

在 MATLAB Online 中打开

hello Alberto

happy new year !!

for your problem above, simply increase Fd for the second method until you get the expected results

file : code_v3.m

%% test 3 : with find_delaunay_boundary03_fig1 
% (from Fex : https://fr.mathworks.com/matlabcentral/fileexchange/60690-boundary-extraction-identification-and-tracing-from-point-cloud-data?s_tid=ta_fx_results ) 
% Fd = 1.5; %Fd = dmax (max point to point distance)
Fd = 3; %Fd = dmax (max point to point distance)

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Answer 2

Alberto Acri 2024-6-12

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在 MATLAB Online 中打开

Hi @Mathieu NOE

I tried using the various functions to retrieve the outermost nodes for other types of curves like the ones attached. Of these the best would (probably) be the one attached.

Do you happen to know if there is a possibility to identify the 'Fd' parameter automatically so as to retrieve exactly the outer nodes?

Here is the code:

    %data
    PARAMETER = 0.2;
    
    % ===============
    
    x = data(:,1);
    y = data(:,2);
    
    % (from Fex : https://fr.mathworks.com/matlabcentral/fileexchange/60690-boundary-extraction-identification-and-tracing-from-point-cloud-data?s_tid=ta_fx_results ) 
    Fd = PARAMETER; %Fd = dmax (max point to point distance)
    [bids, E, Ne] = find_delaunay_boundary03_fig1(data,Fd); 
    
    x3 = [];
    y3 = [];
    for ck = 1:numel(bids)
        x3 = [x3; x(bids{ck})];
        y3 = [y3; y(bids{ck})];
    end
    
    nodes_ext_2D = [x3, y3];
    
    figure
    plot(data(:,1),data(:,2),'r.','MarkerSize',10);
    hold on
    plot(nodes_ext_2D(:,1),nodes_ext_2D(:,2),'k.','MarkerSize',10);
    hold off

Here two examples: on the left a 'simple' curve but I can't retrieve all the outer nodes; on the right a more complicated case.

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Mathieu NOE 2024-6-12

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hello again

I tried a few things , but it seems we are always a bit loo low or too high, so either we pick too many points or not enough (so we get this small gaps as you show on the left picture). I am not sure there is a magical way to solve that issue - or we need another approach , like how to identify the inner zig zags and remove them.

here what I have tried , based on the averaged distance between points and defining the Fd value from there.

with data_1 it works well , for other cases it's not so good

%data
    
    % ===============
    data = data_7;
    x = data(:,1);
    y = data(:,2);
    
    % Execute boundary
    k=boundary(x(:),y(:),1);
    xk = x(k);
    yk = y(k);
    dx = mean(abs(diff(xk)));
    dy = mean(abs(diff(yk)));
    dr = sqrt(dx.^2 + dy.^2);
    PARAMETER = 5*dr;
    
%     dx = max(abs(diff(x)));
%     dy = max(abs(diff(y)));
%     dr = sqrt(dx.^2 + dy.^2);
%     PARAMETER = 10*dr;
    
    % (from Fex : https://fr.mathworks.com/matlabcentral/fileexchange/60690-boundary-extraction-identification-and-tracing-from-point-cloud-data?s_tid=ta_fx_results ) 
    Fd = PARAMETER; %Fd = dmax (max point to point distance)
    [bids, E, Ne] = find_delaunay_boundary03_fig1(data,Fd); 
    
    x3 = [];
    y3 = [];
    for ck = 1:numel(bids)
        x3 = [x3; x(bids{ck})];
        y3 = [y3; y(bids{ck})];
    end
    
    nodes_ext_2D = [x3, y3];
    
    figure
    plot(data(:,1),data(:,2),'r.','MarkerSize',10);
    hold on
    plot(xk,yk,'g*','MarkerSize',10);
    plot(nodes_ext_2D(:,1),nodes_ext_2D(:,2),'k.','MarkerSize',10);
    hold off
    legend('raw data','boudary S = 1','delaunay boudary');

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plot some nodes from an .txt file

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plot some nodes from an .txt file

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