Matlab | Application Package Repository Telkom University

Category: Matlab

Category Archives: Matlab

Matlab News Blog From https://blogs.mathworks.com/

VerifyEqual within a time interval

/ 2024-08-01 / Matlab

Hello, I have been assigned a series of tests to be completed on several floating point variables. The assignment requires me to check whether the Expected value is the same as the Actual value. However, it may take a few milliseconds for the Actual value to reach the same status as the Expected value, and the verifyEqual function does not seem to consider this scenario.
I’d like to know if there is some sort of workaround for this situation. Basically, if A is the Actual value and E the Expected value, I’d like to modify (somehow) the verifyEqual function so that it returns a passing test if A == E with a time delay of approx. 3 milliseconds.

Thank you for your cooperation.Hello, I have been assigned a series of tests to be completed on several floating point variables. The assignment requires me to check whether the Expected value is the same as the Actual value. However, it may take a few milliseconds for the Actual value to reach the same status as the Expected value, and the verifyEqual function does not seem to consider this scenario.
I’d like to know if there is some sort of workaround for this situation. Basically, if A is the Actual value and E the Expected value, I’d like to modify (somehow) the verifyEqual function so that it returns a passing test if A == E with a time delay of approx. 3 milliseconds.

Thank you for your cooperation. Hello, I have been assigned a series of tests to be completed on several floating point variables. The assignment requires me to check whether the Expected value is the same as the Actual value. However, it may take a few milliseconds for the Actual value to reach the same status as the Expected value, and the verifyEqual function does not seem to consider this scenario.
I’d like to know if there is some sort of workaround for this situation. Basically, if A is the Actual value and E the Expected value, I’d like to modify (somehow) the verifyEqual function so that it returns a passing test if A == E with a time delay of approx. 3 milliseconds.

Thank you for your cooperation. matlab function, testcase, guide, solve, time MATLAB Answers — New Questions

Adjust the fitting for non-unique values data (alternative to the fuction smoothing spline, createFit.f)

/ 2024-08-01 / Matlab

Good morning everyone,
I am trying to extract the fracture aperture from a tomography image. I have written the code for the extraction, and everything works using the fitting smoothing spline (createFit function attached) for clu_sli_12.mat and I_12.mat, but not on clu_sli_1400.mat and I_1400.mat in which we have non-unique data (presented directly in the code below). The variable clu_sli_1400 represents the point cloud used as reference for selecting data in the tomography slice (variable I_1400). We tried using other functions in matlab, without success. Can anybody help suggesting a specific function for fitting this data?
thanks in advance for any help!!!!!!!!
clear;
close all;
clc;
warning off;

load(‘I_1400.mat’)
load(‘clu_sli_1400.mat’)

voxel_size=70.69*10^-3;

%% Iteration on the slices
mean_widths = [];
std_widths = [];
min_widths = [];
max_widths = [];
areas = [];

% for ii = 12 %we have to iterate to ii = 12 and ii = 1400 (the last one not working properly on the fitting)

% fitting data
X = clu_sli(:,1);
Y = clu_sli(:,2);

% Create model fitting
[fitresult, gof] = createFit(X, Y);

% Calcolare i valori della curva di fitting
vv = ppval(fitresult.p, X);

% Calcolare la prima derivata della curva di fitting
d1 = ppval(fnder(fitresult.p, 1), X);

% Calcolare la normale alla curva di fitting
epsilon = 1e-10; % Evita la divisione per zero
nn = 1 ./ (d1 + epsilon);

% Calcolare gli angoli delle normali
theta = atan(-1 ./ d1); % Angolo della normale (in radianti)

% Calcolare le componenti della normale
normal_x = cos(theta); % Componente x della normale
normal_y = sin(theta); % Componente y della normale

% Calcolare l’ampiezza della crepa e visualizzare i segmenti
span = 50;
largh = zeros(size(clu_sli, 1), 1);

% Creare una figura con due subplot
figure;

% Primo subplot: Visualizzare la slice con i pallini rossi
subplot(1, 2, 1);
pcolor(I);
shading interp;
axis equal;
hold on;
scatter(clu_sli(:,1), clu_sli(:,2), ‘r’);
xlabel(‘X’);
ylabel(‘Y’);

% Secondo subplot: Visualizzare la slice con i segmenti verdi
subplot(1, 2, 2);
pcolor(I);
shading interp;
axis equal;
hold on;
scatter(clu_sli(:,1), clu_sli(:,2), ‘r’);
plot(X, vv, ‘b’, ‘LineWidth’, 2);
quiver(X, vv, normal_x, normal_y, 0.5, ‘k’, ‘LineWidth’, 1);

intensity_profiles = cell(size(clu_sli, 1), 1);
vet_in = zeros(size(clu_sli, 1), 2);
vet_fin = zeros(size(clu_sli, 1), 2);

for kk = 1:size(clu_sli, 1)
c = round(X(kk));
r = round(vv(kk));

% Genera i pixel lungo la normale
x_profile = round(c + (-span:span) * normal_x(kk));
y_profile = round(r + (-span:span) * normal_y(kk));

% Assicurarsi che i pixel siano all’interno dell’immagine
valid_idx = x_profile > 0 & x_profile <= size(I, 2) & y_profile > 0 & y_profile <= size(I, 1);
x_profile = x_profile(valid_idx);
y_profile = y_profile(valid_idx);

% Estrarre i valori del profilo dall’immagine
profilo = I(sub2ind(size(I), y_profile, x_profile));
intensity_profiles{kk} = profilo;

% Calcolare l’ampiezza della crepa
largh(kk) = conv_crepa(profilo);

% Calcolare i punti del segmento di larghezza
half_width = largh(kk) / 2;
segment_x = [c – half_width * normal_x(kk), c + half_width * normal_x(kk)];
segment_y = [r – half_width * normal_y(kk), r + half_width * normal_y(kk)];

vet_in(kk,1) = c – half_width * normal_x(kk);
vet_fin(kk,1) = c + half_width * normal_x(kk);

vet_in(kk,2) = r – half_width * normal_y(kk);
vet_fin(kk,2) = r + half_width * normal_y(kk);

% Visualizzare il segmento di larghezza
plot(segment_x, segment_y, ‘g’, ‘LineWidth’, 2);
end

hold off;

% Calcolare la media, la deviazione standard, il minimo e il massimo della larghezza della crepa
mean_width = mean(largh, ‘omitnan’);
std_width = std(largh, ‘omitnan’);
min_width = min(largh, [], ‘omitnan’);
max_width = max(largh, [], ‘omitnan’);

mean_widths = [mean_widths; mean_width];
std_widths = [std_widths; std_width];
min_widths = [min_widths; min_width];
max_widths = [max_widths; max_width];

% Calcolo dell’area all’interno delle curve usando trapz
% Creazione del poligono per calcolare l’area
all_x = [vet_in(:,1); flip(vet_fin(:,1))];
all_y = [vet_in(:,2); flip(vet_fin(:,2))];

% Rimuovi i NaN
valid_idx = ~isnan(all_x) & ~isnan(all_y);
all_x = all_x(valid_idx);
all_y = all_y(valid_idx);

% Assicurarsi che i punti formino un poligono chiuso
if length(all_x) > 1 && (all_x(1) ~= all_x(end) || all_y(1) ~= all_y(end))
all_x(end+1) = all_x(1);
all_y(end+1) = all_y(1);
end

% Calcolo dell’area del poligono usando trapz
area = trapz(all_x, all_y);
areas = [areas; area];

title(‘Area Between Curves per Slice’);

%% calcoli in mm

areas_mm = areas.* voxel_size^2
std_mm=std_widths.*voxel_size
mean_mm=mean_widths.*voxel_size
min_mm=min_widths.*voxel_size
max_mm=max_widths.*voxel_sizeGood morning everyone,
I am trying to extract the fracture aperture from a tomography image. I have written the code for the extraction, and everything works using the fitting smoothing spline (createFit function attached) for clu_sli_12.mat and I_12.mat, but not on clu_sli_1400.mat and I_1400.mat in which we have non-unique data (presented directly in the code below). The variable clu_sli_1400 represents the point cloud used as reference for selecting data in the tomography slice (variable I_1400). We tried using other functions in matlab, without success. Can anybody help suggesting a specific function for fitting this data?
thanks in advance for any help!!!!!!!!
clear;
close all;
clc;
warning off;