I define a sparse identity matrix, using the SPEYE command as follows:

A = speye(5)

and receive the following output:

A =

(1,1) 1

(2,2) 1

(3,3) 1

(4,4) 1

(5,5) 1

I would like to write the non-zero elements as well as the row and column information corresponding to the non-zero elements to a text file.I define a sparse identity matrix, using the SPEYE command as follows:

A = speye(5)

and receive the following output:

A =

(1,1) 1

(2,2) 1

(3,3) 1

(4,4) 1

(5,5) 1

I would like to write the non-zero elements as well as the row and column information corresponding to the non-zero elements to a text file. I define a sparse identity matrix, using the SPEYE command as follows:

A = speye(5)

and receive the following output:

A =

(1,1) 1

(2,2) 1

(3,3) 1

(4,4) 1

(5,5) 1

I would like to write the non-zero elements as well as the row and column information corresponding to the non-zero elements to a text file.  MATLAB Answers — New Questions

​

I’m using Matlab/Simulink 2018b and my model is not runnable. The following error appears: The output of ‘path of chart XYZ’ cannot be logged because this block is a Simulink function within Stateflow or a MATLAB Function block. Even though no signal is logged. I don’t know how to localize the issue. Befor puschen the model to the lib it was working fine. How can I fix the issue?I’m using Matlab/Simulink 2018b and my model is not runnable. The following error appears: The output of ‘path of chart XYZ’ cannot be logged because this block is a Simulink function within Stateflow or a MATLAB Function block. Even though no signal is logged. I don’t know how to localize the issue. Befor puschen the model to the lib it was working fine. How can I fix the issue? I’m using Matlab/Simulink 2018b and my model is not runnable. The following error appears: The output of ‘path of chart XYZ’ cannot be logged because this block is a Simulink function within Stateflow or a MATLAB Function block. Even though no signal is logged. I don’t know how to localize the issue. Befor puschen the model to the lib it was working fine. How can I fix the issue? output cannot be logged MATLAB Answers — New Questions

​

I am attempting to plot stictly horizontal freestream flow using fcontour and the Symbolic Math toolbox. A few days ago I was able to successfully achieve this with the code below. However, starting last night whenever I run the script the "symbolic method" produces vertical contour lines that indicate stricty vertical freestram flow. I have gone back to the original code to ensure nothing I changed in the script had an impact, but the issue still remains. I have included an image of the incorrect plot (from the symbolic method) vs. the correct plot (from the numerical method) to better illustrate the problem. I would love some guidance on what to try and fix this strange issue.

clear
clc
% use the symbolic method to create the stream function for freestream flow
% and plot it
syms xs ys
Uval = 1;
Vval = 0;
psi_fs = Uval*ys – Vval*xs;
figure(4)
fcontour(psi_fs,[-3 3 -5 5],’k’); %’k’ makes all the lines black
% use the numerical method to create the stream function for freestream
% flow and plot it
xlocs = [-3:.1:3]; % you can use linspace for this
ylocs = [-3:.1:3];
% make the grid of points x horizontal, y vertical
[xmat,ymat] = meshgrid(xlocs,ylocs);
psi_vals_fs = Uval*ymat – Vval*xmat;
figure(5)
contour(xmat, ymat, psi_vals_fs,’k’) % ‘k’ makes all the lines blackI am attempting to plot stictly horizontal freestream flow using fcontour and the Symbolic Math toolbox. A few days ago I was able to successfully achieve this with the code below. However, starting last night whenever I run the script the "symbolic method" produces vertical contour lines that indicate stricty vertical freestram flow. I have gone back to the original code to ensure nothing I changed in the script had an impact, but the issue still remains. I have included an image of the incorrect plot (from the symbolic method) vs. the correct plot (from the numerical method) to better illustrate the problem. I would love some guidance on what to try and fix this strange issue.

clear
clc
% use the symbolic method to create the stream function for freestream flow
% and plot it
syms xs ys
Uval = 1;
Vval = 0;
psi_fs = Uval*ys – Vval*xs;
figure(4)
fcontour(psi_fs,[-3 3 -5 5],’k’); %’k’ makes all the lines black
% use the numerical method to create the stream function for freestream
% flow and plot it
xlocs = [-3:.1:3]; % you can use linspace for this
ylocs = [-3:.1:3];
% make the grid of points x horizontal, y vertical
[xmat,ymat] = meshgrid(xlocs,ylocs);
psi_vals_fs = Uval*ymat – Vval*xmat;
figure(5)
contour(xmat, ymat, psi_vals_fs,’k’) % ‘k’ makes all the lines black I am attempting to plot stictly horizontal freestream flow using fcontour and the Symbolic Math toolbox. A few days ago I was able to successfully achieve this with the code below. However, starting last night whenever I run the script the "symbolic method" produces vertical contour lines that indicate stricty vertical freestram flow. I have gone back to the original code to ensure nothing I changed in the script had an impact, but the issue still remains. I have included an image of the incorrect plot (from the symbolic method) vs. the correct plot (from the numerical method) to better illustrate the problem. I would love some guidance on what to try and fix this strange issue.

clear
clc
% use the symbolic method to create the stream function for freestream flow
% and plot it
syms xs ys
Uval = 1;
Vval = 0;
psi_fs = Uval*ys – Vval*xs;
figure(4)
fcontour(psi_fs,[-3 3 -5 5],’k’); %’k’ makes all the lines black
% use the numerical method to create the stream function for freestream
% flow and plot it
xlocs = [-3:.1:3]; % you can use linspace for this
ylocs = [-3:.1:3];
% make the grid of points x horizontal, y vertical
[xmat,ymat] = meshgrid(xlocs,ylocs);
psi_vals_fs = Uval*ymat – Vval*xmat;
figure(5)
contour(xmat, ymat, psi_vals_fs,’k’) % ‘k’ makes all the lines black fcontour, symbolic MATLAB Answers — New Questions

​

I have a constitutive model with 4 parameters to fit to experimental stress vs strain data. Before implementing the GA, I tried lsqnonlin (with and without multistart), but one of the main problems is that the algorithm often tends to assign bound values to the optimised parameters. To the first parameter, which is larger than the others, it assigns the upper bound, to the second and third it assigns the lower bounds. Plotting the best individuals over time, it appears that the ga spans little between the solutions, and the optimisation seems to be biased towards the first parameter.
I am using the uniaxial model proposed by Dong, and have rechecked the correctness of the model form several times. Below is the main containing the ga and my error function. Any help is welcome, thank you

%CHANGE LINE 3 TO SELECT THE PATCH
%CHANGE LINES 9 AND 10 TO SELECT THE SPECIMEN
clc; clear all;close all;
load ("data_11122.mat"); % change lot number if necessary
clearvars -except data

%Model requires lambda= x/x0 instead of epsilon: calculate lambda
% from exp data and store in data struct

stress=data(6).stress; %change index to change dogbone if necessary
stress=stress/max(stress);
strain=data(6).strain; %change index to change dogbone if necessary
p_initial=[50 25 5 .5]*10e-3;
a0=[1 0 0];
%vincoli su zeta imposti da dong ( zeta tra 0 e 1 )
lb=[0 0 0 0]; %% AGGIUSTA UNITA DIN MISURA
ub=[650 98 35 1];

%ottimizzazione non lineare con metodo least square ( like Dong )
options = optimoptions(‘ga’, ‘Display’, ‘iter’, ‘PopulationSize’, 100, ‘MaxGenerations’, 200, ‘UseParallel’, true,’NonlinearConstraintAlgorithm’,’penalty’,’PlotFcn’,’gaplotbestindiv’,’InitialPopulationMatrix’,p_initial);
nvars=4;
[p_opt, fval, exitflag, output] = ga(@(p)(error_function(p, strain, stress)), nvars, [], [], [], [], lb, ub, [], options);

%check plot e ricostruzione del modello con i parametri ottimizzati
c = p_opt(1); % c
k1 = p_opt(2); % k1
k2 = p_opt(3); % k2
zeta = p_opt(4); % zeta
for i=1:length(strain)

sigma_calculated(i)=c*(strain(i)^2) + 2*strain(i)^2*k1*((strain(i)^2-1)*(i-zeta)^2*exp(k2*(strain(i)^2-1)^2*(1-zeta)^2));

end

% for i=1:length(strain)
% f = [strain(i) 0 0; %deformation gradient for each value of strain
% 0 1/sqrt(strain(i)) 0;
% 0 0 1/sqrt(strain(i))];
% C = f .* f’; % Calcola il tensore di Cauchy-Green
% prodot = a0 .* a0′;
% I4 = sum(sum(C .* prodot)); % calcolo invariante di C
% I = eye(3); % Matrice identità
%
% sigma= f*c*I*f’ + 2*f*(k1 * (1 – zeta)^2 * (I4 – 1) * (exp(k2 * ((1 – zeta) * (I4 – 1))^2)) * prodot)*f’;
%
% sigma_calculated(i)= sigma(1,1); %componente xx
% end

figure()
plot (strain,stress);
hold on
plot(strain, sigma_calculated,’r’);
legend (‘experimental’, ‘model’,’Location’, ‘northwest’);

function error = error_function(p,strain,stress)

sigma_calculated = zeros(length(strain), 1);

for i = 2:length(strain) %avoiding first value=0;

% f = [strain(i) 0 0; %deformation gradient
% 0 1/sqrt(strain(i)) 0;
% 0 0 1/sqrt(strain(i))];
% C = f .* f’; % tensore di Cauchy-Green
% prodot = a0 .* a0′;
% I4 = a0′ .* C .* a0; % calcolo IV invariante di C
% I = eye(3);
%
% sigma= f*p(1)*I*f’ + 2*f*(p(2) * (1 – p(4))^2 * (I4 – 1) * (exp(p(3) * ((1 – p(4)) * (I4 – 1))^2)) * prodot)*f’;
%
%
% sigma_calculated(i)= sigma(1,1); %componente xx
sigma_calculated(i)=p(1)*(strain(i)^2) + 2*strain(i)^2*p(2)*((strain(i)^2-1)*(i-p(4))^2*exp(p(3)*(strain(i)^2-1)^2*(1-p(4))^2));

end

error= sum((sigma_calculated-stress).^2) ; %/sum(stress).^2; % Somma dei quadrati delle differenze
%aggiungere normalizzazione rispetto allo stress( guardare script marta calcolo error )
endI have a constitutive model with 4 parameters to fit to experimental stress vs strain data. Before implementing the GA, I tried lsqnonlin (with and without multistart), but one of the main problems is that the algorithm often tends to assign bound values to the optimised parameters. To the first parameter, which is larger than the others, it assigns the upper bound, to the second and third it assigns the lower bounds. Plotting the best individuals over time, it appears that the ga spans little between the solutions, and the optimisation seems to be biased towards the first parameter.
I am using the uniaxial model proposed by Dong, and have rechecked the correctness of the model form several times. Below is the main containing the ga and my error function. Any help is welcome, thank you

%CHANGE LINE 3 TO SELECT THE PATCH
%CHANGE LINES 9 AND 10 TO SELECT THE SPECIMEN
clc; clear all;close all;
load ("data_11122.mat"); % change lot number if necessary
clearvars -except data

%Model requires lambda= x/x0 instead of epsilon: calculate lambda
% from exp data and store in data struct

stress=data(6).stress; %change index to change dogbone if necessary
stress=stress/max(stress);
strain=data(6).strain; %change index to change dogbone if necessary
p_initial=[50 25 5 .5]*10e-3;
a0=[1 0 0];
%vincoli su zeta imposti da dong ( zeta tra 0 e 1 )
lb=[0 0 0 0]; %% AGGIUSTA UNITA DIN MISURA
ub=[650 98 35 1];

%ottimizzazione non lineare con metodo least square ( like Dong )
options = optimoptions(‘ga’, ‘Display’, ‘iter’, ‘PopulationSize’, 100, ‘MaxGenerations’, 200, ‘UseParallel’, true,’NonlinearConstraintAlgorithm’,’penalty’,’PlotFcn’,’gaplotbestindiv’,’InitialPopulationMatrix’,p_initial);
nvars=4;
[p_opt, fval, exitflag, output] = ga(@(p)(error_function(p, strain, stress)), nvars, [], [], [], [], lb, ub, [], options);

%check plot e ricostruzione del modello con i parametri ottimizzati
c = p_opt(1); % c
k1 = p_opt(2); % k1
k2 = p_opt(3); % k2
zeta = p_opt(4); % zeta
for i=1:length(strain)

sigma_calculated(i)=c*(strain(i)^2) + 2*strain(i)^2*k1*((strain(i)^2-1)*(i-zeta)^2*exp(k2*(strain(i)^2-1)^2*(1-zeta)^2));

end

% for i=1:length(strain)
% f = [strain(i) 0 0; %deformation gradient for each value of strain
% 0 1/sqrt(strain(i)) 0;
% 0 0 1/sqrt(strain(i))];
% C = f .* f’; % Calcola il tensore di Cauchy-Green
% prodot = a0 .* a0′;
% I4 = sum(sum(C .* prodot)); % calcolo invariante di C
% I = eye(3); % Matrice identità
%
% sigma= f*c*I*f’ + 2*f*(k1 * (1 – zeta)^2 * (I4 – 1) * (exp(k2 * ((1 – zeta) * (I4 – 1))^2)) * prodot)*f’;
%
% sigma_calculated(i)= sigma(1,1); %componente xx
% end

figure()
plot (strain,stress);
hold on
plot(strain, sigma_calculated,’r’);
legend (‘experimental’, ‘model’,’Location’, ‘northwest’);

function error = error_function(p,strain,stress)

sigma_calculated = zeros(length(strain), 1);

for i = 2:length(strain) %avoiding first value=0;

% f = [strain(i) 0 0; %deformation gradient
% 0 1/sqrt(strain(i)) 0;
% 0 0 1/sqrt(strain(i))];
% C = f .* f’; % tensore di Cauchy-Green
% prodot = a0 .* a0′;
% I4 = a0′ .* C .* a0; % calcolo IV invariante di C
% I = eye(3);
%
% sigma= f*p(1)*I*f’ + 2*f*(p(2) * (1 – p(4))^2 * (I4 – 1) * (exp(p(3) * ((1 – p(4)) * (I4 – 1))^2)) * prodot)*f’;
%
%
% sigma_calculated(i)= sigma(1,1); %componente xx
sigma_calculated(i)=p(1)*(strain(i)^2) + 2*strain(i)^2*p(2)*((strain(i)^2-1)*(i-p(4))^2*exp(p(3)*(strain(i)^2-1)^2*(1-p(4))^2));

end

error= sum((sigma_calculated-stress).^2) ; %/sum(stress).^2; % Somma dei quadrati delle differenze
%aggiungere normalizzazione rispetto allo stress( guardare script marta calcolo error )
end I have a constitutive model with 4 parameters to fit to experimental stress vs strain data. Before implementing the GA, I tried lsqnonlin (with and without multistart), but one of the main problems is that the algorithm often tends to assign bound values to the optimised parameters. To the first parameter, which is larger than the others, it assigns the upper bound, to the second and third it assigns the lower bounds. Plotting the best individuals over time, it appears that the ga spans little between the solutions, and the optimisation seems to be biased towards the first parameter.
I am using the uniaxial model proposed by Dong, and have rechecked the correctness of the model form several times. Below is the main containing the ga and my error function. Any help is welcome, thank you

%CHANGE LINE 3 TO SELECT THE PATCH
%CHANGE LINES 9 AND 10 TO SELECT THE SPECIMEN
clc; clear all;close all;
load ("data_11122.mat"); % change lot number if necessary
clearvars -except data

%Model requires lambda= x/x0 instead of epsilon: calculate lambda
% from exp data and store in data struct

stress=data(6).stress; %change index to change dogbone if necessary
stress=stress/max(stress);
strain=data(6).strain; %change index to change dogbone if necessary
p_initial=[50 25 5 .5]*10e-3;
a0=[1 0 0];
%vincoli su zeta imposti da dong ( zeta tra 0 e 1 )
lb=[0 0 0 0]; %% AGGIUSTA UNITA DIN MISURA
ub=[650 98 35 1];

%ottimizzazione non lineare con metodo least square ( like Dong )
options = optimoptions(‘ga’, ‘Display’, ‘iter’, ‘PopulationSize’, 100, ‘MaxGenerations’, 200, ‘UseParallel’, true,’NonlinearConstraintAlgorithm’,’penalty’,’PlotFcn’,’gaplotbestindiv’,’InitialPopulationMatrix’,p_initial);
nvars=4;
[p_opt, fval, exitflag, output] = ga(@(p)(error_function(p, strain, stress)), nvars, [], [], [], [], lb, ub, [], options);

%check plot e ricostruzione del modello con i parametri ottimizzati
c = p_opt(1); % c
k1 = p_opt(2); % k1
k2 = p_opt(3); % k2
zeta = p_opt(4); % zeta
for i=1:length(strain)

sigma_calculated(i)=c*(strain(i)^2) + 2*strain(i)^2*k1*((strain(i)^2-1)*(i-zeta)^2*exp(k2*(strain(i)^2-1)^2*(1-zeta)^2));

end

% for i=1:length(strain)
% f = [strain(i) 0 0; %deformation gradient for each value of strain
% 0 1/sqrt(strain(i)) 0;
% 0 0 1/sqrt(strain(i))];
% C = f .* f’; % Calcola il tensore di Cauchy-Green
% prodot = a0 .* a0′;
% I4 = sum(sum(C .* prodot)); % calcolo invariante di C
% I = eye(3); % Matrice identità
%
% sigma= f*c*I*f’ + 2*f*(k1 * (1 – zeta)^2 * (I4 – 1) * (exp(k2 * ((1 – zeta) * (I4 – 1))^2)) * prodot)*f’;
%
% sigma_calculated(i)= sigma(1,1); %componente xx
% end

figure()
plot (strain,stress);
hold on
plot(strain, sigma_calculated,’r’);
legend (‘experimental’, ‘model’,’Location’, ‘northwest’);

function error = error_function(p,strain,stress)

sigma_calculated = zeros(length(strain), 1);

for i = 2:length(strain) %avoiding first value=0;

% f = [strain(i) 0 0; %deformation gradient
% 0 1/sqrt(strain(i)) 0;
% 0 0 1/sqrt(strain(i))];
% C = f .* f’; % tensore di Cauchy-Green
% prodot = a0 .* a0′;
% I4 = a0′ .* C .* a0; % calcolo IV invariante di C
% I = eye(3);
%
% sigma= f*p(1)*I*f’ + 2*f*(p(2) * (1 – p(4))^2 * (I4 – 1) * (exp(p(3) * ((1 – p(4)) * (I4 – 1))^2)) * prodot)*f’;
%
%
% sigma_calculated(i)= sigma(1,1); %componente xx
sigma_calculated(i)=p(1)*(strain(i)^2) + 2*strain(i)^2*p(2)*((strain(i)^2-1)*(i-p(4))^2*exp(p(3)*(strain(i)^2-1)^2*(1-p(4))^2));

end

error= sum((sigma_calculated-stress).^2) ; %/sum(stress).^2; % Somma dei quadrati delle differenze
%aggiungere normalizzazione rispetto allo stress( guardare script marta calcolo error )
end ga, optimization, nonlinear, curve fitting MATLAB Answers — New Questions

​

Dear community,
I fing the geoglobe and geoplot3 functions reallly nice and powerful, yet I have been trying to plot a simple case without succeeding.
I want to plot lines that are located below the surface (underground tunnels, wells, etc.). Is there a workaround to play with the ground layer "transparency" to see those lines that are drawn efectively below, but hidden by the surface?
An extract of the code I’m using:
uif = uifigure;
lat=[43.93365285 43.94086810];
lon=[5.48483510 5.48376465];
alt=[483.591 499.640];
g = geoglobe(uif,’Basemap’,’satellite’,"Terrain","gmted2010");
geoplot3(g,lat,lon,alt,’r’,"LineWidth",2)
campos(g,43.912,5.465,700);
campitch(g,0)
camheading(g,20)
Thanks a lot in advance.Dear community,
I fing the geoglobe and geoplot3 functions reallly nice and powerful, yet I have been trying to plot a simple case without succeeding.
I want to plot lines that are located below the surface (underground tunnels, wells, etc.). Is there a workaround to play with the ground layer "transparency" to see those lines that are drawn efectively below, but hidden by the surface?
An extract of the code I’m using:
uif = uifigure;
lat=[43.93365285 43.94086810];
lon=[5.48483510 5.48376465];
alt=[483.591 499.640];
g = geoglobe(uif,’Basemap’,’satellite’,"Terrain","gmted2010");
geoplot3(g,lat,lon,alt,’r’,"LineWidth",2)
campos(g,43.912,5.465,700);
campitch(g,0)
camheading(g,20)
Thanks a lot in advance. Dear community,
I fing the geoglobe and geoplot3 functions reallly nice and powerful, yet I have been trying to plot a simple case without succeeding.
I want to plot lines that are located below the surface (underground tunnels, wells, etc.). Is there a workaround to play with the ground layer "transparency" to see those lines that are drawn efectively below, but hidden by the surface?
An extract of the code I’m using:
uif = uifigure;
lat=[43.93365285 43.94086810];
lon=[5.48483510 5.48376465];
alt=[483.591 499.640];
g = geoglobe(uif,’Basemap’,’satellite’,"Terrain","gmted2010");
geoplot3(g,lat,lon,alt,’r’,"LineWidth",2)
campos(g,43.912,5.465,700);
campitch(g,0)
camheading(g,20)
Thanks a lot in advance. geoplot3 MATLAB Answers — New Questions

​

I am using the ‘importdata’ function to read a text file. It reads the numeric data as a matrix and text data as a cell array; now the first element of cell array is all the headers, I use a ‘strsplit’ function to separate each individual variable name in header. The issue is that there are some blank columns in the text file, so the numeric matrix reads it as a ‘NaN’ but the resulting variables (which I used ‘strsplit’ for) eliminates blanks. Now the result is that size(textdata,2) +size(data,2) is not equal to size(variable,2). I want to read blanks in the header as a variable, alias it as ‘empty’I am using the ‘importdata’ function to read a text file. It reads the numeric data as a matrix and text data as a cell array; now the first element of cell array is all the headers, I use a ‘strsplit’ function to separate each individual variable name in header. The issue is that there are some blank columns in the text file, so the numeric matrix reads it as a ‘NaN’ but the resulting variables (which I used ‘strsplit’ for) eliminates blanks. Now the result is that size(textdata,2) +size(data,2) is not equal to size(variable,2). I want to read blanks in the header as a variable, alias it as ‘empty’ I am using the ‘importdata’ function to read a text file. It reads the numeric data as a matrix and text data as a cell array; now the first element of cell array is all the headers, I use a ‘strsplit’ function to separate each individual variable name in header. The issue is that there are some blank columns in the text file, so the numeric matrix reads it as a ‘NaN’ but the resulting variables (which I used ‘strsplit’ for) eliminates blanks. Now the result is that size(textdata,2) +size(data,2) is not equal to size(variable,2). I want to read blanks in the header as a variable, alias it as ‘empty’ textscan, imporatdata, strsplit MATLAB Answers — New Questions

​

I am using the "fileread" function to read a text file. What encoding is used when reading this file?I am using the "fileread" function to read a text file. What encoding is used when reading this file? I am using the "fileread" function to read a text file. What encoding is used when reading this file? file, i/o, fileread, fopen MATLAB Answers — New Questions

​

I have a Git repository, and when I try to use Source Control integration in MATLAB, it always asks for a password. I have installed the ssh keys, and those work fine in Git for Windows terminal. The keys are placed in the correct location. I have the correct "HOME" environment variable. The output of command "git config –list":

diff.astextplain.textconv=astextplain
filter.lfs.clean=git-lfs clean — %f
filter.lfs.smudge=git-lfs smudge — %f
filter.lfs.process=git-lfs filter-process
filter.lfs.required=true
http.sslbackend=openssl
http.sslcainfo=C:/Program Files/Git/mingw64/ssl/certs/ca-bundle.crt
core.autocrlf=true
core.fscache=true
core.symlinks=false
credential.helper=manager
user.name=****************
user.email=****************core.symlinks=false
core.repositoryformatversion=0
core.filemode=false
core.logallrefupdates=trueremote.origin.url=http://****************.com/test/matlab.gitremote.origin.fetch=+refs/heads/*:refs/remotes/origin/*
branch.master.remote=origin
branch.master.merge=refs/heads/master
How to get rid of the password prompt?I have a Git repository, and when I try to use Source Control integration in MATLAB, it always asks for a password. I have installed the ssh keys, and those work fine in Git for Windows terminal. The keys are placed in the correct location. I have the correct "HOME" environment variable. The output of command "git config –list":

diff.astextplain.textconv=astextplain
filter.lfs.clean=git-lfs clean — %f
filter.lfs.smudge=git-lfs smudge — %f
filter.lfs.process=git-lfs filter-process
filter.lfs.required=true
http.sslbackend=openssl
http.sslcainfo=C:/Program Files/Git/mingw64/ssl/certs/ca-bundle.crt
core.autocrlf=true
core.fscache=true
core.symlinks=false
credential.helper=manager
user.name=****************
user.email=****************core.symlinks=false
core.repositoryformatversion=0
core.filemode=false
core.logallrefupdates=trueremote.origin.url=http://****************.com/test/matlab.gitremote.origin.fetch=+refs/heads/*:refs/remotes/origin/*
branch.master.remote=origin
branch.master.merge=refs/heads/master
How to get rid of the password prompt? I have a Git repository, and when I try to use Source Control integration in MATLAB, it always asks for a password. I have installed the ssh keys, and those work fine in Git for Windows terminal. The keys are placed in the correct location. I have the correct "HOME" environment variable. The output of command "git config –list":

diff.astextplain.textconv=astextplain
filter.lfs.clean=git-lfs clean — %f
filter.lfs.smudge=git-lfs smudge — %f
filter.lfs.process=git-lfs filter-process
filter.lfs.required=true
http.sslbackend=openssl
http.sslcainfo=C:/Program Files/Git/mingw64/ssl/certs/ca-bundle.crt
core.autocrlf=true
core.fscache=true
core.symlinks=false
credential.helper=manager
user.name=****************
user.email=****************core.symlinks=false
core.repositoryformatversion=0
core.filemode=false
core.logallrefupdates=trueremote.origin.url=http://****************.com/test/matlab.gitremote.origin.fetch=+refs/heads/*:refs/remotes/origin/*
branch.master.remote=origin
branch.master.merge=refs/heads/master
How to get rid of the password prompt? git, repository, source, control, integration, ssh, password, http MATLAB Answers — New Questions

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I want to generate plain code out of my keras deep learning neuronal network to avoid the dependency of third-party libs. I do this in MATLAB R2021a.
After that, I would like to build an S-Function for the use inside a model in MATLAB R2017b for deployment in HIL, which is set up in MATLAB R2017b. What is the recommended workflow?I want to generate plain code out of my keras deep learning neuronal network to avoid the dependency of third-party libs. I do this in MATLAB R2021a.
After that, I would like to build an S-Function for the use inside a model in MATLAB R2017b for deployment in HIL, which is set up in MATLAB R2017b. What is the recommended workflow? I want to generate plain code out of my keras deep learning neuronal network to avoid the dependency of third-party libs. I do this in MATLAB R2021a.
After that, I would like to build an S-Function for the use inside a model in MATLAB R2017b for deployment in HIL, which is set up in MATLAB R2017b. What is the recommended workflow?  MATLAB Answers — New Questions

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I would like to nest/superimpose a plot on another, which is part of a tiledlayout and spans multiple tiles. The following works fine:
figure
tiledlayout(1, 6)

nexttile([1 5])
plot(1:10)

nexttile(6)
plot(11:20)
However, the following does not and it results in two figures:
figure
tiledlayout(1, 6)

nexttile([1 5])
plot(1:10)

axes("position", [0.5 0.5 0.2 0.2])
plot(21:30)

nexttile(6)
plot(11:20)I would like to nest/superimpose a plot on another, which is part of a tiledlayout and spans multiple tiles. The following works fine:
figure
tiledlayout(1, 6)

nexttile([1 5])
plot(1:10)

nexttile(6)
plot(11:20)
However, the following does not and it results in two figures:
figure
tiledlayout(1, 6)

nexttile([1 5])
plot(1:10)

axes("position", [0.5 0.5 0.2 0.2])
plot(21:30)

nexttile(6)
plot(11:20) I would like to nest/superimpose a plot on another, which is part of a tiledlayout and spans multiple tiles. The following works fine:
figure
tiledlayout(1, 6)

nexttile([1 5])
plot(1:10)

nexttile(6)
plot(11:20)
However, the following does not and it results in two figures:
figure
tiledlayout(1, 6)

nexttile([1 5])
plot(1:10)

axes("position", [0.5 0.5 0.2 0.2])
plot(21:30)

nexttile(6)
plot(11:20) plotting, tiledlayout, axes, tile MATLAB Answers — New Questions

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