I want to view Solver Profiler output for a variety of Simulink simulations, and currently I am running them one by one in the Solver Profiler window to achieve this. I would like to batch run them overnight and view profiler results afterwards, is this possible?I want to view Solver Profiler output for a variety of Simulink simulations, and currently I am running them one by one in the Solver Profiler window to achieve this. I would like to batch run them overnight and view profiler results afterwards, is this possible? I want to view Solver Profiler output for a variety of Simulink simulations, and currently I am running them one by one in the Solver Profiler window to achieve this. I would like to batch run them overnight and view profiler results afterwards, is this possible? simulink profiler, command line MATLAB Answers — New Questions

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I want to launch MATLAB via the .NET Engine API from a C# application to run my Simulink models. My Simulink models are created using a variety of MATLAB versions (e.g., R2023b and R2025b). Can my C# application launch an instance of R2023b using the MATLAB .NET Engine API,  perform some processing, close R2023b, and then launch an R2025b instance?I want to launch MATLAB via the .NET Engine API from a C# application to run my Simulink models. My Simulink models are created using a variety of MATLAB versions (e.g., R2023b and R2025b). Can my C# application launch an instance of R2023b using the MATLAB .NET Engine API,  perform some processing, close R2023b, and then launch an R2025b instance? I want to launch MATLAB via the .NET Engine API from a C# application to run my Simulink models. My Simulink models are created using a variety of MATLAB versions (e.g., R2023b and R2025b). Can my C# application launch an instance of R2023b using the MATLAB .NET Engine API,  perform some processing, close R2023b, and then launch an R2025b instance? .net, c#, engine, api, multiple, multi MATLAB Answers — New Questions

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In MATLAB R2025b, listdlg appears to return the selected indices in the order the user clicked them, rather than in ascending numeric order.
In older versions such as R2022b and R2024a, the returned indices were ordered ascendingly, which made the behavior predictable and easy to work with in downstream code.
This change caused nasty bugs in existing code on my side, because I use the returned indices to index into related lists that assume a consistent ascending order. With the new behavior, the item order can differ depending on click sequence, which breaks code that previously worked correctly.
Minimal example:
[idx, tf] = listdlg( …
‘ListString’, {‘A’,’B’,’C’,’D’,’E’}, …
‘SelectionMode’, ‘multiple’);
disp(idx)
In R2025b, if I click items in the order 2, 1, 3, the result is: [2 1 3] -> Which leads to calling [‘B’, ‘A’, ‘C’] in downstream code
Whereas in older releases it was always: [1 2 3]
Questions:
Is this behavior change intentional in R2025b? I cannot find any documentation on that.
Is there a way to restore the old ascending-order behavior globally, for example via a preference or in startup.m?
Or is the only safe solution now to wrap every listdlg call with sort(idx) manually?
I can work around this with sort, but it would be very helpful if there were a default compatibility option, especially for larger existing codebases.In MATLAB R2025b, listdlg appears to return the selected indices in the order the user clicked them, rather than in ascending numeric order.
In older versions such as R2022b and R2024a, the returned indices were ordered ascendingly, which made the behavior predictable and easy to work with in downstream code.
This change caused nasty bugs in existing code on my side, because I use the returned indices to index into related lists that assume a consistent ascending order. With the new behavior, the item order can differ depending on click sequence, which breaks code that previously worked correctly.
Minimal example:
[idx, tf] = listdlg( …
‘ListString’, {‘A’,’B’,’C’,’D’,’E’}, …
‘SelectionMode’, ‘multiple’);
disp(idx)
In R2025b, if I click items in the order 2, 1, 3, the result is: [2 1 3] -> Which leads to calling [‘B’, ‘A’, ‘C’] in downstream code
Whereas in older releases it was always: [1 2 3]
Questions:
Is this behavior change intentional in R2025b? I cannot find any documentation on that.
Is there a way to restore the old ascending-order behavior globally, for example via a preference or in startup.m?
Or is the only safe solution now to wrap every listdlg call with sort(idx) manually?
I can work around this with sort, but it would be very helpful if there were a default compatibility option, especially for larger existing codebases. In MATLAB R2025b, listdlg appears to return the selected indices in the order the user clicked them, rather than in ascending numeric order.
In older versions such as R2022b and R2024a, the returned indices were ordered ascendingly, which made the behavior predictable and easy to work with in downstream code.
This change caused nasty bugs in existing code on my side, because I use the returned indices to index into related lists that assume a consistent ascending order. With the new behavior, the item order can differ depending on click sequence, which breaks code that previously worked correctly.
Minimal example:
[idx, tf] = listdlg( …
‘ListString’, {‘A’,’B’,’C’,’D’,’E’}, …
‘SelectionMode’, ‘multiple’);
disp(idx)
In R2025b, if I click items in the order 2, 1, 3, the result is: [2 1 3] -> Which leads to calling [‘B’, ‘A’, ‘C’] in downstream code
Whereas in older releases it was always: [1 2 3]
Questions:
Is this behavior change intentional in R2025b? I cannot find any documentation on that.
Is there a way to restore the old ascending-order behavior globally, for example via a preference or in startup.m?
Or is the only safe solution now to wrap every listdlg call with sort(idx) manually?
I can work around this with sort, but it would be very helpful if there were a default compatibility option, especially for larger existing codebases. listdlg, order, click, user MATLAB Answers — New Questions

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Context: When plotting images, I have often set the tick marks to face outward, where they are not obscured by the image. This change requires that I nudge the title upward by a small amount so that it is separated from the ticks.
Problem: When I save the file using print, saveas, exportgraphics or printpreview, the title is sometimes missing in the output file. (See attached examples above.)
I found that this problem occurs when the image is plotted using pcolor (and probably surf as well). It does not occur when using plot and imagesc. Annotation is used below to draw an outline of the outer extent of the figure. The nudged title clearly lies within that limit.
Work Around: The problem is fixed by ensuring that the Units property for the title is set to data after adjusting the title position. Note that I have filed a bug report with Matlab.
Versions: This problem did not occur with Matlab versions 2022a and earlier. It started with version 2022b and continues with version 2023a.
Coded example:
function test_figureTitleProblem
%… Simple demonstration of missing title problem.
% When plotting images, I have often set the tick marks to face outward,
% where they are not obscurved by the image. This change requires that
% I nudge the title upward by a small amount so that it is separated
% from the ticks.
%
% Problem: When I save the file using print, saveas, exportgraphics
% and printpreview, the title is missing in the output file.
% I found that this problem occurs when the image is plotted using
% pcolor, and does not occur when imagesc is used. I also found
% that the problem can be fixed by changing the Units property for the title
% back to data (see below). Annotation is used below to draw an outline of
% the outer extent of the figure. The nudged title clearly lies within
% that limit.

%% Initialize variables
%… Load Matlab’s global topographic data
load(‘topo.mat’, ‘topo’);
%… Create grid vectors
lon = 0:359;
lat = -89:90;

%% Plot
figure
%… Missing title occurs with pcolor, and not with imagesc
pcolor(lon, lat, topo)
shading interp
% imagesc(lon, lat, topo)
% axis xy
%… Format plot
hA = gca;
hA.Box = ‘on’;
hA.TickDir = ‘out’; % Oriented tick marks to face outward
hA.Layer = ‘top’; % Places grid lines and tick markers on top of image
hA.FontSize = 16;
hA.LineWidth = 1;
%… Render title, and nudge upward to separate from outward tick marks
hT = title(‘Demonstration of Demonstration of Missing Title’, ‘FontSize’, 16);
hT.Units = ‘normalized’;
hT.Position(2) = hT.Position(2) + 0.02;
% hT.Units = ‘data’; % Uncomment this line to fix problem
%… Draw rectangle to show outer limit for figure
annotation("rectangle", hA.OuterPosition, Color="red", LineWidth=1)
%… Start print preview to show that the title is missing
print(‘missingTitle_print.pdf’, ‘-dpdf’)
saveas(hA, ‘missingTitle_saveas.pdf’)
exportgraphics(hA, ‘missingTitle_exportgraphics.pdf’)
printpreview

endContext: When plotting images, I have often set the tick marks to face outward, where they are not obscured by the image. This change requires that I nudge the title upward by a small amount so that it is separated from the ticks.
Problem: When I save the file using print, saveas, exportgraphics or printpreview, the title is sometimes missing in the output file. (See attached examples above.)
I found that this problem occurs when the image is plotted using pcolor (and probably surf as well). It does not occur when using plot and imagesc. Annotation is used below to draw an outline of the outer extent of the figure. The nudged title clearly lies within that limit.
Work Around: The problem is fixed by ensuring that the Units property for the title is set to data after adjusting the title position. Note that I have filed a bug report with Matlab.
Versions: This problem did not occur with Matlab versions 2022a and earlier. It started with version 2022b and continues with version 2023a.
Coded example:
function test_figureTitleProblem
%… Simple demonstration of missing title problem.
% When plotting images, I have often set the tick marks to face outward,
% where they are not obscurved by the image. This change requires that
% I nudge the title upward by a small amount so that it is separated
% from the ticks.
%
% Problem: When I save the file using print, saveas, exportgraphics
% and printpreview, the title is missing in the output file.
% I found that this problem occurs when the image is plotted using
% pcolor, and does not occur when imagesc is used. I also found
% that the problem can be fixed by changing the Units property for the title
% back to data (see below). Annotation is used below to draw an outline of
% the outer extent of the figure. The nudged title clearly lies within
% that limit.

%% Initialize variables
%… Load Matlab’s global topographic data
load(‘topo.mat’, ‘topo’);
%… Create grid vectors
lon = 0:359;
lat = -89:90;

%% Plot
figure
%… Missing title occurs with pcolor, and not with imagesc
pcolor(lon, lat, topo)
shading interp
% imagesc(lon, lat, topo)
% axis xy
%… Format plot
hA = gca;
hA.Box = ‘on’;
hA.TickDir = ‘out’; % Oriented tick marks to face outward
hA.Layer = ‘top’; % Places grid lines and tick markers on top of image
hA.FontSize = 16;
hA.LineWidth = 1;
%… Render title, and nudge upward to separate from outward tick marks
hT = title(‘Demonstration of Demonstration of Missing Title’, ‘FontSize’, 16);
hT.Units = ‘normalized’;
hT.Position(2) = hT.Position(2) + 0.02;
% hT.Units = ‘data’; % Uncomment this line to fix problem
%… Draw rectangle to show outer limit for figure
annotation("rectangle", hA.OuterPosition, Color="red", LineWidth=1)
%… Start print preview to show that the title is missing
print(‘missingTitle_print.pdf’, ‘-dpdf’)
saveas(hA, ‘missingTitle_saveas.pdf’)
exportgraphics(hA, ‘missingTitle_exportgraphics.pdf’)
printpreview

end Context: When plotting images, I have often set the tick marks to face outward, where they are not obscured by the image. This change requires that I nudge the title upward by a small amount so that it is separated from the ticks.
Problem: When I save the file using print, saveas, exportgraphics or printpreview, the title is sometimes missing in the output file. (See attached examples above.)
I found that this problem occurs when the image is plotted using pcolor (and probably surf as well). It does not occur when using plot and imagesc. Annotation is used below to draw an outline of the outer extent of the figure. The nudged title clearly lies within that limit.
Work Around: The problem is fixed by ensuring that the Units property for the title is set to data after adjusting the title position. Note that I have filed a bug report with Matlab.
Versions: This problem did not occur with Matlab versions 2022a and earlier. It started with version 2022b and continues with version 2023a.
Coded example:
function test_figureTitleProblem
%… Simple demonstration of missing title problem.
% When plotting images, I have often set the tick marks to face outward,
% where they are not obscurved by the image. This change requires that
% I nudge the title upward by a small amount so that it is separated
% from the ticks.
%
% Problem: When I save the file using print, saveas, exportgraphics
% and printpreview, the title is missing in the output file.
% I found that this problem occurs when the image is plotted using
% pcolor, and does not occur when imagesc is used. I also found
% that the problem can be fixed by changing the Units property for the title
% back to data (see below). Annotation is used below to draw an outline of
% the outer extent of the figure. The nudged title clearly lies within
% that limit.

%% Initialize variables
%… Load Matlab’s global topographic data
load(‘topo.mat’, ‘topo’);
%… Create grid vectors
lon = 0:359;
lat = -89:90;

%% Plot
figure
%… Missing title occurs with pcolor, and not with imagesc
pcolor(lon, lat, topo)
shading interp
% imagesc(lon, lat, topo)
% axis xy
%… Format plot
hA = gca;
hA.Box = ‘on’;
hA.TickDir = ‘out’; % Oriented tick marks to face outward
hA.Layer = ‘top’; % Places grid lines and tick markers on top of image
hA.FontSize = 16;
hA.LineWidth = 1;
%… Render title, and nudge upward to separate from outward tick marks
hT = title(‘Demonstration of Demonstration of Missing Title’, ‘FontSize’, 16);
hT.Units = ‘normalized’;
hT.Position(2) = hT.Position(2) + 0.02;
% hT.Units = ‘data’; % Uncomment this line to fix problem
%… Draw rectangle to show outer limit for figure
annotation("rectangle", hA.OuterPosition, Color="red", LineWidth=1)
%… Start print preview to show that the title is missing
print(‘missingTitle_print.pdf’, ‘-dpdf’)
saveas(hA, ‘missingTitle_saveas.pdf’)
exportgraphics(hA, ‘missingTitle_exportgraphics.pdf’)
printpreview

end missing title MATLAB Answers — New Questions

​

Which parts of this code is correct? I’m getting confused on which sections are relevant for my question above?
N = length(sig1);
xdft1 = fft(sig1);
xdft1 = xdft1(1:N/2+1);
psdx1 = (1/(Fs*N)) * abs(xdft1).^2;
psdx1(2:end-1) = 2*psdx1(2:end-1);
freq1 = 0:Fs/length(sig1):Fs/2;
P1_band = psdx1(freq1 >= 20 & freq1 <= 100);
f1_band = freq1(freq1 >= 20 & freq1 <= 100);
power_sig1 = trapz(f1_band, P1_band);

noise = x_new – x_notch;
SNR_dB = snr(x_notch, noise);
P_signal = mean(signalSeg.^2, ‘omitnan’);
P_noise = mean(noiseSeg.^2, ‘omitnan’);
SNR_dB = 10*log10(P_signal / P_noise);

f_notch = 60;
BW_notch = 2;
wo = f_notch/(Fs/2);
Q = f_notch/BW_notch;
bw = wo/Q;
[b_n,a_n] = iirnotch(wo,bw);
x_notch = filtfilt(b_n,a_n,x_bp);

x = x(:);
t = (0:length(x)-1)’/Fs;
% Frequency analysis
x_dm = x – mean(x);
N = length(x_dm);
X = fft(x_dm);
K = floor(N/2);
X = X(1:K+1);
PSD = (1/(Fs*N)) * abs(X).^2;
if K > 1
PSD(2:end-1) = 2*PSD(2:end-1);
end
f = (0:K)’ * (Fs/N);
% Find dominant frequency
[~, idxPeak] = max(PSD(2:end)); % ignore 0 Hz
idxPeak = idxPeak + 1;
domFreq = f(idxPeak);
fprintf(‘Dominant frequency = %.2f Hzn’, domFreq);Which parts of this code is correct? I’m getting confused on which sections are relevant for my question above?
N = length(sig1);
xdft1 = fft(sig1);
xdft1 = xdft1(1:N/2+1);
psdx1 = (1/(Fs*N)) * abs(xdft1).^2;
psdx1(2:end-1) = 2*psdx1(2:end-1);
freq1 = 0:Fs/length(sig1):Fs/2;
P1_band = psdx1(freq1 >= 20 & freq1 <= 100);
f1_band = freq1(freq1 >= 20 & freq1 <= 100);
power_sig1 = trapz(f1_band, P1_band);

noise = x_new – x_notch;
SNR_dB = snr(x_notch, noise);
P_signal = mean(signalSeg.^2, ‘omitnan’);
P_noise = mean(noiseSeg.^2, ‘omitnan’);
SNR_dB = 10*log10(P_signal / P_noise);

f_notch = 60;
BW_notch = 2;
wo = f_notch/(Fs/2);
Q = f_notch/BW_notch;
bw = wo/Q;
[b_n,a_n] = iirnotch(wo,bw);
x_notch = filtfilt(b_n,a_n,x_bp);

x = x(:);
t = (0:length(x)-1)’/Fs;
% Frequency analysis
x_dm = x – mean(x);
N = length(x_dm);
X = fft(x_dm);
K = floor(N/2);
X = X(1:K+1);
PSD = (1/(Fs*N)) * abs(X).^2;
if K > 1
PSD(2:end-1) = 2*PSD(2:end-1);
end
f = (0:K)’ * (Fs/N);
% Find dominant frequency
[~, idxPeak] = max(PSD(2:end)); % ignore 0 Hz
idxPeak = idxPeak + 1;
domFreq = f(idxPeak);
fprintf(‘Dominant frequency = %.2f Hzn’, domFreq); Which parts of this code is correct? I’m getting confused on which sections are relevant for my question above?
N = length(sig1);
xdft1 = fft(sig1);
xdft1 = xdft1(1:N/2+1);
psdx1 = (1/(Fs*N)) * abs(xdft1).^2;
psdx1(2:end-1) = 2*psdx1(2:end-1);
freq1 = 0:Fs/length(sig1):Fs/2;
P1_band = psdx1(freq1 >= 20 & freq1 <= 100);
f1_band = freq1(freq1 >= 20 & freq1 <= 100);
power_sig1 = trapz(f1_band, P1_band);

noise = x_new – x_notch;
SNR_dB = snr(x_notch, noise);
P_signal = mean(signalSeg.^2, ‘omitnan’);
P_noise = mean(noiseSeg.^2, ‘omitnan’);
SNR_dB = 10*log10(P_signal / P_noise);

f_notch = 60;
BW_notch = 2;
wo = f_notch/(Fs/2);
Q = f_notch/BW_notch;
bw = wo/Q;
[b_n,a_n] = iirnotch(wo,bw);
x_notch = filtfilt(b_n,a_n,x_bp);

x = x(:);
t = (0:length(x)-1)’/Fs;
% Frequency analysis
x_dm = x – mean(x);
N = length(x_dm);
X = fft(x_dm);
K = floor(N/2);
X = X(1:K+1);
PSD = (1/(Fs*N)) * abs(X).^2;
if K > 1
PSD(2:end-1) = 2*PSD(2:end-1);
end
f = (0:K)’ * (Fs/N);
% Find dominant frequency
[~, idxPeak] = max(PSD(2:end)); % ignore 0 Hz
idxPeak = idxPeak + 1;
domFreq = f(idxPeak);
fprintf(‘Dominant frequency = %.2f Hzn’, domFreq); bandpower MATLAB Answers — New Questions

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Basic discontinuety was simulated 0Hz to 40Ghz 40000 samples as presented in the photos . S-parameters were extracted as S2P file(attached in the link). My goal is to see the discontinuety using time domain reflectometry. what is the proper way to do IFFT in this case? What windows should I multiply with? Thanks.Basic discontinuety was simulated 0Hz to 40Ghz 40000 samples as presented in the photos . S-parameters were extracted as S2P file(attached in the link). My goal is to see the discontinuety using time domain reflectometry. what is the proper way to do IFFT in this case? What windows should I multiply with? Thanks. Basic discontinuety was simulated 0Hz to 40Ghz 40000 samples as presented in the photos . S-parameters were extracted as S2P file(attached in the link). My goal is to see the discontinuety using time domain reflectometry. what is the proper way to do IFFT in this case? What windows should I multiply with? Thanks. ifft, tdr, s-parameters MATLAB Answers — New Questions

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Elapsed time is 8.597842 seconds.

clear, clc
tic
load(‘Erros_a.mat’); load(‘Sp1_b.mat’);
Relatorio = zeros(5000,4,’uint32′);
Relatorio_final = zeros(10000,4,’uint32′);
a = 10;
for i1 = 1:1
% BLOCO 1 — usa cache se existir, senão gera e salva
cache_file = sprintf(‘Erros_b_cache_%d.mat’, i1);
if exist(cache_file, ‘file’)
load(cache_file);
else
linha_a = Erros_a(a,:);
vals = linha_a(linha_a > 0);
vals = vals(1:end-2);
nRef = numel(vals);
cols_b1 = 2*(double(vals)-1) + 1;
cols_b2 = 2*(double(vals)-1) + 2;
passa = (Sp1_b(:, cols_b1) == 0) & (Sp1_b(:, cols_b2) == 0);
col_dest_matrix = uint32(cumsum(passa, 2) – passa + 1);
Erros_b = zeros(3268760, 37, ‘uint32’);
for d = 1:nRef
linhas_ok = find(passa(:, d));
if ~isempty(linhas_ok)
idx = sub2ind([3268760, 37], linhas_ok, double(col_dest_matrix(linhas_ok, d)));
Erros_b(idx) = vals(d);
end
end
save(cache_file, ‘Erros_b’, ‘-v7.3’);
end
% BLOCO 2 — preparação
load(‘Sp1_c.mat’);
Erros_b(:,38) = (1:size(Erros_b,1))’;
mascara = Erros_b(:, 13) > 50;
Erros_reduzido = Erros_b(mascara, :);
[lin_u, col_u] = find(Sp1_c == 1);
uns_nas_colunas = accumarray(col_u, lin_u, [size(Sp1_c,2) 1], @(x){sort(x)}, {[]});
clear Erros_b Sp1_c lin_u col_u mascara;
[~, ordem] = sortrows(Erros_reduzido(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido(ordem, :);
[~, ordem] = sortrows(Erros_reduzido_ordem(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido_ordem(ordem, :);
clear Erros_reduzido ordem;
ERO = double(Erros_reduzido_ordem);
clear Erros_reduzido_ordem;
nCols_c = numel(uns_nas_colunas);
uns_sets = cell(nCols_c, 1);
for cc = 1:nCols_c
s = uns_nas_colunas{cc};
if ~isempty(s)
m = false(max(s), 1);
m(s) = true;
uns_sets{cc} = m;
else
uns_sets{cc} = false(0,1);
end
end
% BLOCO 3 — while principal
num_linhas = size(ERO, 1);
y = 1;
offset = 0;
while (offset + 1) <= num_linhas
i = offset + 1;
linha_at = ERO(i, 1:38);
a_valid = linha_at(linha_at > 0 & linha_at <= 100);
% k=2: dois ponteiros
A = uns_nas_colunas{a_valid(1)};
B = uns_nas_colunas{a_valid(2)};
buf = zeros(min(numel(A), numel(B)), 1);
ia = 1; ib = 1; ic = 0;
na = numel(A); nb = numel(B);
while ia <= na && ib <= nb
if A(ia) == B(ib)
ic = ic + 1; buf(ic) = A(ia);
ia = ia + 1; ib = ib + 1;
elseif A(ia) < B(ib); ia = ia + 1;
else; ib = ib + 1;
end
end
candidatas = buf(1:ic);
w=1; z=1;
n(w,1) = a_valid(w,1);
achou = false;
if isempty(candidatas)
melhor_erro = a_valid(2) – 1;
melhor_linha = linha_at(38);
n(w,2) = a_valid(w,2);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = A(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
end
% k>=3: lookup lógico
for k = 3:length(a_valid)
if achou; break; end
n(w,k-1) = a_valid(w,k-1);
col_atual = a_valid(k);
s2 = uns_sets{col_atual};
if isempty(candidatas) || isempty(s2)
proximas = [];
else
mask = candidatas <= numel(s2);
cand_validas = candidatas(mask);
if isempty(cand_validas); proximas = [];
else; proximas = cand_validas(s2(cand_validas)); end
end
if isempty(proximas)
melhor_erro = col_atual – 1;
melhor_linha = linha_at(38);
n(w,k) = a_valid(w,k);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = candidatas(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
break;
end
candidatas = proximas;
melhor_linha = candidatas(1);
end % for k

if ~achou; offset = offset + 1; end
end % while
clear ERO uns_sets;
Relatorio = sortrows(Relatorio, 4, ‘descend’);
Relatorio_final(i1,:) = Relatorio(1,:);
Relatorio(:,:) = 0;
a = a + 1;
end % i1
tocElapsed time is 8.597842 seconds.

clear, clc
tic
load(‘Erros_a.mat’); load(‘Sp1_b.mat’);
Relatorio = zeros(5000,4,’uint32′);
Relatorio_final = zeros(10000,4,’uint32′);
a = 10;
for i1 = 1:1
% BLOCO 1 — usa cache se existir, senão gera e salva
cache_file = sprintf(‘Erros_b_cache_%d.mat’, i1);
if exist(cache_file, ‘file’)
load(cache_file);
else
linha_a = Erros_a(a,:);
vals = linha_a(linha_a > 0);
vals = vals(1:end-2);
nRef = numel(vals);
cols_b1 = 2*(double(vals)-1) + 1;
cols_b2 = 2*(double(vals)-1) + 2;
passa = (Sp1_b(:, cols_b1) == 0) & (Sp1_b(:, cols_b2) == 0);
col_dest_matrix = uint32(cumsum(passa, 2) – passa + 1);
Erros_b = zeros(3268760, 37, ‘uint32’);
for d = 1:nRef
linhas_ok = find(passa(:, d));
if ~isempty(linhas_ok)
idx = sub2ind([3268760, 37], linhas_ok, double(col_dest_matrix(linhas_ok, d)));
Erros_b(idx) = vals(d);
end
end
save(cache_file, ‘Erros_b’, ‘-v7.3’);
end
% BLOCO 2 — preparação
load(‘Sp1_c.mat’);
Erros_b(:,38) = (1:size(Erros_b,1))’;
mascara = Erros_b(:, 13) > 50;
Erros_reduzido = Erros_b(mascara, :);
[lin_u, col_u] = find(Sp1_c == 1);
uns_nas_colunas = accumarray(col_u, lin_u, [size(Sp1_c,2) 1], @(x){sort(x)}, {[]});
clear Erros_b Sp1_c lin_u col_u mascara;
[~, ordem] = sortrows(Erros_reduzido(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido(ordem, :);
[~, ordem] = sortrows(Erros_reduzido_ordem(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido_ordem(ordem, :);
clear Erros_reduzido ordem;
ERO = double(Erros_reduzido_ordem);
clear Erros_reduzido_ordem;
nCols_c = numel(uns_nas_colunas);
uns_sets = cell(nCols_c, 1);
for cc = 1:nCols_c
s = uns_nas_colunas{cc};
if ~isempty(s)
m = false(max(s), 1);
m(s) = true;
uns_sets{cc} = m;
else
uns_sets{cc} = false(0,1);
end
end
% BLOCO 3 — while principal
num_linhas = size(ERO, 1);
y = 1;
offset = 0;
while (offset + 1) <= num_linhas
i = offset + 1;
linha_at = ERO(i, 1:38);
a_valid = linha_at(linha_at > 0 & linha_at <= 100);
% k=2: dois ponteiros
A = uns_nas_colunas{a_valid(1)};
B = uns_nas_colunas{a_valid(2)};
buf = zeros(min(numel(A), numel(B)), 1);
ia = 1; ib = 1; ic = 0;
na = numel(A); nb = numel(B);
while ia <= na && ib <= nb
if A(ia) == B(ib)
ic = ic + 1; buf(ic) = A(ia);
ia = ia + 1; ib = ib + 1;
elseif A(ia) < B(ib); ia = ia + 1;
else; ib = ib + 1;
end
end
candidatas = buf(1:ic);
w=1; z=1;
n(w,1) = a_valid(w,1);
achou = false;
if isempty(candidatas)
melhor_erro = a_valid(2) – 1;
melhor_linha = linha_at(38);
n(w,2) = a_valid(w,2);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = A(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
end
% k>=3: lookup lógico
for k = 3:length(a_valid)
if achou; break; end
n(w,k-1) = a_valid(w,k-1);
col_atual = a_valid(k);
s2 = uns_sets{col_atual};
if isempty(candidatas) || isempty(s2)
proximas = [];
else
mask = candidatas <= numel(s2);
cand_validas = candidatas(mask);
if isempty(cand_validas); proximas = [];
else; proximas = cand_validas(s2(cand_validas)); end
end
if isempty(proximas)
melhor_erro = col_atual – 1;
melhor_linha = linha_at(38);
n(w,k) = a_valid(w,k);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = candidatas(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
break;
end
candidatas = proximas;
melhor_linha = candidatas(1);
end % for k

if ~achou; offset = offset + 1; end
end % while
clear ERO uns_sets;
Relatorio = sortrows(Relatorio, 4, ‘descend’);
Relatorio_final(i1,:) = Relatorio(1,:);
Relatorio(:,:) = 0;
a = a + 1;
end % i1
toc Elapsed time is 8.597842 seconds.

clear, clc
tic
load(‘Erros_a.mat’); load(‘Sp1_b.mat’);
Relatorio = zeros(5000,4,’uint32′);
Relatorio_final = zeros(10000,4,’uint32′);
a = 10;
for i1 = 1:1
% BLOCO 1 — usa cache se existir, senão gera e salva
cache_file = sprintf(‘Erros_b_cache_%d.mat’, i1);
if exist(cache_file, ‘file’)
load(cache_file);
else
linha_a = Erros_a(a,:);
vals = linha_a(linha_a > 0);
vals = vals(1:end-2);
nRef = numel(vals);
cols_b1 = 2*(double(vals)-1) + 1;
cols_b2 = 2*(double(vals)-1) + 2;
passa = (Sp1_b(:, cols_b1) == 0) & (Sp1_b(:, cols_b2) == 0);
col_dest_matrix = uint32(cumsum(passa, 2) – passa + 1);
Erros_b = zeros(3268760, 37, ‘uint32’);
for d = 1:nRef
linhas_ok = find(passa(:, d));
if ~isempty(linhas_ok)
idx = sub2ind([3268760, 37], linhas_ok, double(col_dest_matrix(linhas_ok, d)));
Erros_b(idx) = vals(d);
end
end
save(cache_file, ‘Erros_b’, ‘-v7.3’);
end
% BLOCO 2 — preparação
load(‘Sp1_c.mat’);
Erros_b(:,38) = (1:size(Erros_b,1))’;
mascara = Erros_b(:, 13) > 50;
Erros_reduzido = Erros_b(mascara, :);
[lin_u, col_u] = find(Sp1_c == 1);
uns_nas_colunas = accumarray(col_u, lin_u, [size(Sp1_c,2) 1], @(x){sort(x)}, {[]});
clear Erros_b Sp1_c lin_u col_u mascara;
[~, ordem] = sortrows(Erros_reduzido(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido(ordem, :);
[~, ordem] = sortrows(Erros_reduzido_ordem(:, 1:38));
Erros_reduzido_ordem = Erros_reduzido_ordem(ordem, :);
clear Erros_reduzido ordem;
ERO = double(Erros_reduzido_ordem);
clear Erros_reduzido_ordem;
nCols_c = numel(uns_nas_colunas);
uns_sets = cell(nCols_c, 1);
for cc = 1:nCols_c
s = uns_nas_colunas{cc};
if ~isempty(s)
m = false(max(s), 1);
m(s) = true;
uns_sets{cc} = m;
else
uns_sets{cc} = false(0,1);
end
end
% BLOCO 3 — while principal
num_linhas = size(ERO, 1);
y = 1;
offset = 0;
while (offset + 1) <= num_linhas
i = offset + 1;
linha_at = ERO(i, 1:38);
a_valid = linha_at(linha_at > 0 & linha_at <= 100);
% k=2: dois ponteiros
A = uns_nas_colunas{a_valid(1)};
B = uns_nas_colunas{a_valid(2)};
buf = zeros(min(numel(A), numel(B)), 1);
ia = 1; ib = 1; ic = 0;
na = numel(A); nb = numel(B);
while ia <= na && ib <= nb
if A(ia) == B(ib)
ic = ic + 1; buf(ic) = A(ia);
ia = ia + 1; ib = ib + 1;
elseif A(ia) < B(ib); ia = ia + 1;
else; ib = ib + 1;
end
end
candidatas = buf(1:ic);
w=1; z=1;
n(w,1) = a_valid(w,1);
achou = false;
if isempty(candidatas)
melhor_erro = a_valid(2) – 1;
melhor_linha = linha_at(38);
n(w,2) = a_valid(w,2);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = A(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
end
% k>=3: lookup lógico
for k = 3:length(a_valid)
if achou; break; end
n(w,k-1) = a_valid(w,k-1);
col_atual = a_valid(k);
s2 = uns_sets{col_atual};
if isempty(candidatas) || isempty(s2)
proximas = [];
else
mask = candidatas <= numel(s2);
cand_validas = candidatas(mask);
if isempty(cand_validas); proximas = [];
else; proximas = cand_validas(s2(cand_validas)); end
end
if isempty(proximas)
melhor_erro = col_atual – 1;
melhor_linha = linha_at(38);
n(w,k) = a_valid(w,k);
nP = length(n);
lo = offset + 1; hi = num_linhas; ultlin_abs = offset;
if all(ERO(lo, 1:nP) == n)
while lo <= hi
mid = floor((lo + hi) / 2);
if all(ERO(mid, 1:nP) == n)
ultlin_abs = mid; lo = mid + 1;
else; hi = mid – 1; end
end
end
if ultlin_abs > offset; offset = ultlin_abs;
else; offset = offset + 1; end
n = [];
Relatorio(y,1) = Erros_a(i1,40);
Relatorio(y,2) = candidatas(1);
Relatorio(y,3) = melhor_linha;
Relatorio(y,4) = melhor_erro;
y = y + 1;
achou = true;
break;
end
candidatas = proximas;
melhor_linha = candidatas(1);
end % for k

if ~achou; offset = offset + 1; end
end % while
clear ERO uns_sets;
Relatorio = sortrows(Relatorio, 4, ‘descend’);
Relatorio_final(i1,:) = Relatorio(1,:);
Relatorio(:,:) = 0;
a = a + 1;
end % i1
toc script slow MATLAB Answers — New Questions

​

Using Share to "Standalone Desktop App" in MATLAB App Designer, Is there a way to sign the installer .exe file that is generated, and also sign the application .exe file that is generated after the installation file is run? Currently, when using App Designer to package the app, it creates a build of the app and then packages it into an installation .exe file in the same workflow.

The issue I’m forseeing is that if I sign the installation .exe file, the application .exe file within will still be un-signed when it is generated after the installer is run. Am I right in thinking this? If so, is there a method of ensuring the application .exe file within is signed?Using Share to "Standalone Desktop App" in MATLAB App Designer, Is there a way to sign the installer .exe file that is generated, and also sign the application .exe file that is generated after the installation file is run? Currently, when using App Designer to package the app, it creates a build of the app and then packages it into an installation .exe file in the same workflow.

The issue I’m forseeing is that if I sign the installation .exe file, the application .exe file within will still be un-signed when it is generated after the installer is run. Am I right in thinking this? If so, is there a method of ensuring the application .exe file within is signed? Using Share to "Standalone Desktop App" in MATLAB App Designer, Is there a way to sign the installer .exe file that is generated, and also sign the application .exe file that is generated after the installation file is run? Currently, when using App Designer to package the app, it creates a build of the app and then packages it into an installation .exe file in the same workflow.

The issue I’m forseeing is that if I sign the installation .exe file, the application .exe file within will still be un-signed when it is generated after the installer is run. Am I right in thinking this? If so, is there a method of ensuring the application .exe file within is signed? app designer, compiler, certificate, signing MATLAB Answers — New Questions

​

Hi all, I was looking for ways to relabel my x-axis as pi increments for trig functions and was wondering which one would be most effective.
This is what chatgpt gave, but I wanted to see if there are any better ways to achieve the same result. TIA!!
clc, clearvars

x = linspace(0,2*pi,200);
y = sin(x);

% Plot
figure
hold on
plot(x, y)

% Pi Labeling
xt = 0:pi/2:2*pi;
xticks(xt)
label = compose(‘%g\pi’, xt/pi);
xticklabels(label)Hi all, I was looking for ways to relabel my x-axis as pi increments for trig functions and was wondering which one would be most effective.
This is what chatgpt gave, but I wanted to see if there are any better ways to achieve the same result. TIA!!
clc, clearvars

x = linspace(0,2*pi,200);
y = sin(x);

% Plot
figure
hold on
plot(x, y)

% Pi Labeling
xt = 0:pi/2:2*pi;
xticks(xt)
label = compose(‘%g\pi’, xt/pi);
xticklabels(label) Hi all, I was looking for ways to relabel my x-axis as pi increments for trig functions and was wondering which one would be most effective.
This is what chatgpt gave, but I wanted to see if there are any better ways to achieve the same result. TIA!!
clc, clearvars

x = linspace(0,2*pi,200);
y = sin(x);

% Plot
figure
hold on
plot(x, y)

% Pi Labeling
xt = 0:pi/2:2*pi;
xticks(xt)
label = compose(‘%g\pi’, xt/pi);
xticklabels(label) xtick, label, trigonometry, pi MATLAB Answers — New Questions

​

So, I’ve been trying to do 3 variable modeling in MATLAB. I have four sets of data (3 variables and one answer), and I’m trying to bet an equation modeling the data. When running the section of the code pasted below, I keep getting "Index exceeds number of array elements. Index must not exceed 37". There are only 35 variables in the code, so I’m not sure why this isn’t working. As far as why I chose this format, I think x and z can be modeled by a quadratic individually, but I figured each variable needed to be the same power, so they’re all 4th power. Any help would be greatly appreciated.

ft = fittype([ …
‘p000 + p100*x + p010*y + p001*z + ‘ …
‘p200*x.^2 + p020*y.^2 + p002*z.^2 + ‘ …
‘p110*x*y + p101*x*z + p011*y*z + ‘ …
‘p300*x.^3 + p030*y.^3 + p003*z.^3 + ‘ …
‘p210*x.^2*y + p201*x.^2*z + p120*x*y.^2 + ‘ …
‘p021*y.^2*z + p102*x*z.^2 + p012*y*z.^2 + ‘ …
‘p111*x*y*z + ‘ …
‘p400*x.^4 + p040*y.^4 + p004*z.^4 + ‘ …
‘p310*x.^3*y + p301*x.^3*z + p130*x*y.^3 + ‘ …
‘p031*y.^3*z + p103*x*z.^3 + p013*y*z.^3 + ‘ …
‘p220*x.^2*y.^2 + p202*x.^2*z.^2 + p022*y.^2*z.^2 + ‘ …
‘p211*x.^2*y*z + p121*x*y.^2*z + p112*x*y*z.^2’], …
‘independent’, {‘x’, ‘y’, ‘z’}, …
‘dependent’, ‘v’);

[fitobject, gof, output] = fit([x, y, z], v, ft);

disp(fitobject)So, I’ve been trying to do 3 variable modeling in MATLAB. I have four sets of data (3 variables and one answer), and I’m trying to bet an equation modeling the data. When running the section of the code pasted below, I keep getting "Index exceeds number of array elements. Index must not exceed 37". There are only 35 variables in the code, so I’m not sure why this isn’t working. As far as why I chose this format, I think x and z can be modeled by a quadratic individually, but I figured each variable needed to be the same power, so they’re all 4th power. Any help would be greatly appreciated.

ft = fittype([ …
‘p000 + p100*x + p010*y + p001*z + ‘ …
‘p200*x.^2 + p020*y.^2 + p002*z.^2 + ‘ …
‘p110*x*y + p101*x*z + p011*y*z + ‘ …
‘p300*x.^3 + p030*y.^3 + p003*z.^3 + ‘ …
‘p210*x.^2*y + p201*x.^2*z + p120*x*y.^2 + ‘ …
‘p021*y.^2*z + p102*x*z.^2 + p012*y*z.^2 + ‘ …
‘p111*x*y*z + ‘ …
‘p400*x.^4 + p040*y.^4 + p004*z.^4 + ‘ …
‘p310*x.^3*y + p301*x.^3*z + p130*x*y.^3 + ‘ …
‘p031*y.^3*z + p103*x*z.^3 + p013*y*z.^3 + ‘ …
‘p220*x.^2*y.^2 + p202*x.^2*z.^2 + p022*y.^2*z.^2 + ‘ …
‘p211*x.^2*y*z + p121*x*y.^2*z + p112*x*y*z.^2’], …
‘independent’, {‘x’, ‘y’, ‘z’}, …
‘dependent’, ‘v’);

[fitobject, gof, output] = fit([x, y, z], v, ft);

disp(fitobject) So, I’ve been trying to do 3 variable modeling in MATLAB. I have four sets of data (3 variables and one answer), and I’m trying to bet an equation modeling the data. When running the section of the code pasted below, I keep getting "Index exceeds number of array elements. Index must not exceed 37". There are only 35 variables in the code, so I’m not sure why this isn’t working. As far as why I chose this format, I think x and z can be modeled by a quadratic individually, but I figured each variable needed to be the same power, so they’re all 4th power. Any help would be greatly appreciated.

ft = fittype([ …
‘p000 + p100*x + p010*y + p001*z + ‘ …
‘p200*x.^2 + p020*y.^2 + p002*z.^2 + ‘ …
‘p110*x*y + p101*x*z + p011*y*z + ‘ …
‘p300*x.^3 + p030*y.^3 + p003*z.^3 + ‘ …
‘p210*x.^2*y + p201*x.^2*z + p120*x*y.^2 + ‘ …
‘p021*y.^2*z + p102*x*z.^2 + p012*y*z.^2 + ‘ …
‘p111*x*y*z + ‘ …
‘p400*x.^4 + p040*y.^4 + p004*z.^4 + ‘ …
‘p310*x.^3*y + p301*x.^3*z + p130*x*y.^3 + ‘ …
‘p031*y.^3*z + p103*x*z.^3 + p013*y*z.^3 + ‘ …
‘p220*x.^2*y.^2 + p202*x.^2*z.^2 + p022*y.^2*z.^2 + ‘ …
‘p211*x.^2*y*z + p121*x*y.^2*z + p112*x*y*z.^2’], …
‘independent’, {‘x’, ‘y’, ‘z’}, …
‘dependent’, ‘v’);

[fitobject, gof, output] = fit([x, y, z], v, ft);

disp(fitobject) error, curve fitting MATLAB Answers — New Questions

​