function [x,y,err]=muller(f,x0,x1,x2,delta,epsilon,max1)

%Input – f is the object function
% – x0, x1, and x2 are the initial approximations
% – delta is the tolerance for x0, x1, and x2
% – epsilon the the tolerance for the function values y
% – max1 is the maximum number of iterations
%Output- x is the Muller approximation to the zero of f
% – y is the function value y = f(x)
% – err is the error in the approximation of x.

%Initalize the matrices X and Y
X=[x0 x1 x2];
Y=f(X);

%Calculate a and b in formula (15)

for k=1:max1
h0=X(1)-X(3);h1=X(2)-X(3);e0=Y(1)-Y(3);e1=Y(2)-Y(3);c=Y(3);
denom=h1*h0^2-h0*h1^2;
a=(e0*h1-e1*h0)/denom;
b=(e1*h0^2-e0*h1^2)/denom;

%Find the smallest root of (17)
if b < 0
disc=-disc;
end

z=-2*c/(b+disc);
x=X(3)+z;

%Sort the entries of X to find the two closest to x
if abs(x-X(2))<abs(x-X(1))
Q=[X(2) X(1) X(3)];
X=Q;
Y=f(X);
end
if abs(x-X(3))<abs(x-X(2))
R=[X(1) X(3) X(2)];
X=R;
Y=f(X);
end

%Replace the entry of X that was farthest from x with x
X(3)=x;
Y(3) = f(X(3));
y=Y(3);

%Determine stopping criteria
err=abs(z);
relerr=err/(abs(x)+delta);
if (err<delta)||(relerr<delta)||(abs(y)<epsilon)
break
end
endfunction [x,y,err]=muller(f,x0,x1,x2,delta,epsilon,max1)

%Input – f is the object function
% – x0, x1, and x2 are the initial approximations
% – delta is the tolerance for x0, x1, and x2
% – epsilon the the tolerance for the function values y
% – max1 is the maximum number of iterations
%Output- x is the Muller approximation to the zero of f
% – y is the function value y = f(x)
% – err is the error in the approximation of x.

%Initalize the matrices X and Y
X=[x0 x1 x2];
Y=f(X);

%Calculate a and b in formula (15)

for k=1:max1
h0=X(1)-X(3);h1=X(2)-X(3);e0=Y(1)-Y(3);e1=Y(2)-Y(3);c=Y(3);
denom=h1*h0^2-h0*h1^2;
a=(e0*h1-e1*h0)/denom;
b=(e1*h0^2-e0*h1^2)/denom;

%Find the smallest root of (17)
if b < 0
disc=-disc;
end

z=-2*c/(b+disc);
x=X(3)+z;

%Sort the entries of X to find the two closest to x
if abs(x-X(2))<abs(x-X(1))
Q=[X(2) X(1) X(3)];
X=Q;
Y=f(X);
end
if abs(x-X(3))<abs(x-X(2))
R=[X(1) X(3) X(2)];
X=R;
Y=f(X);
end

%Replace the entry of X that was farthest from x with x
X(3)=x;
Y(3) = f(X(3));
y=Y(3);

%Determine stopping criteria
err=abs(z);
relerr=err/(abs(x)+delta);
if (err<delta)||(relerr<delta)||(abs(y)<epsilon)
break
end
end function [x,y,err]=muller(f,x0,x1,x2,delta,epsilon,max1)

%Input – f is the object function
% – x0, x1, and x2 are the initial approximations
% – delta is the tolerance for x0, x1, and x2
% – epsilon the the tolerance for the function values y
% – max1 is the maximum number of iterations
%Output- x is the Muller approximation to the zero of f
% – y is the function value y = f(x)
% – err is the error in the approximation of x.

%Initalize the matrices X and Y
X=[x0 x1 x2];
Y=f(X);

%Calculate a and b in formula (15)

for k=1:max1
h0=X(1)-X(3);h1=X(2)-X(3);e0=Y(1)-Y(3);e1=Y(2)-Y(3);c=Y(3);
denom=h1*h0^2-h0*h1^2;
a=(e0*h1-e1*h0)/denom;
b=(e1*h0^2-e0*h1^2)/denom;

%Find the smallest root of (17)
if b < 0
disc=-disc;
end

z=-2*c/(b+disc);
x=X(3)+z;

%Sort the entries of X to find the two closest to x
if abs(x-X(2))<abs(x-X(1))
Q=[X(2) X(1) X(3)];
X=Q;
Y=f(X);
end
if abs(x-X(3))<abs(x-X(2))
R=[X(1) X(3) X(2)];
X=R;
Y=f(X);
end

%Replace the entry of X that was farthest from x with x
X(3)=x;
Y(3) = f(X(3));
y=Y(3);

%Determine stopping criteria
err=abs(z);
relerr=err/(abs(x)+delta);
if (err<delta)||(relerr<delta)||(abs(y)<epsilon)
break
end
end muller method MATLAB Answers — New Questions

​

An SIR model for COVID-19, Flu, and RSV

We utilize mathematical modeling based on differential equations to investigate the trans-
mission dynamics of the three respiratory infections: COVID-19, Flu, and RSV. To start, let

us consider a simple SIR model, which involves three compartments: the susceptible individ-
uals (denoted by S), the infected individuals (denoted by I), and the recovered individuals

(denoted by R).
The model is described as follows:
dS
dt = Lambda − beta SI − mu S,
dI
dt = beta SI − (gamma + w + mu )I,
dR
dt = gamma I − mu R.

The parameter Lambda is the population influx rate, beta is the transmission rates, mu is the natural
death rate for the human hosts, gamma is the rate of recovery from the infection, and w is the
disease induced death rate. For simplicity, we set Lambda = mu N, where N is the total population.
For the US, mu = 1/76.33 year−1 = 1/(76.33 ∗ 365) day−1

, and N = 345, 426, 571 people.

Other parameter values are:
COVID-19: gamma = 1/14 day−1

, w = 0.01/7 day−1
;

Flu: gamma = 1/3.5 day−1

, w = 0.006/7 day−1
;

RSV: gamma = 1/10.5 day−1

, w = 0.002/7 day−1
.

Apply this model separately to the US data on COVID-19, Flu, and RSV, and estimate the
value of the transmission rate beta in each case. The data fitting is typically done by using the
least squares method. Based on the fitted beta , calculate the basic reproduction number R0
for each.
I am supposed to consider the above instruction to write matlab codes to find the transmission rate for the flu, covid and the RSV data. Each of these diseases has six seasons starting from 2018-2019 seaons to 2023-2024 seasons.
if the matlab data for 2018-2019 flu season is this:
% Filter for the 2018-19 flu season
Flu_data_2018_2019 = Flu_data(strcmp(Flu_data.Season, ‘2018-19’), :);

% Display the filtered data for the 2018-19 season
disp(Flu_data_2018_2019);

% Display the ‘Weekly_Rate’ column (replace with the correct column name if needed)
Weekly_Rate_2018_2019 = Flu_data_2018_2019.WeeklyRate;
disp(Weekly_Rate_2018_2019);
help write a matlab code to find the transmission rate for the 2018-2019 flu season and the reproduction numberAn SIR model for COVID-19, Flu, and RSV

We utilize mathematical modeling based on differential equations to investigate the trans-
mission dynamics of the three respiratory infections: COVID-19, Flu, and RSV. To start, let

us consider a simple SIR model, which involves three compartments: the susceptible individ-
uals (denoted by S), the infected individuals (denoted by I), and the recovered individuals

(denoted by R).
The model is described as follows:
dS
dt = Lambda − beta SI − mu S,
dI
dt = beta SI − (gamma + w + mu )I,
dR
dt = gamma I − mu R.

The parameter Lambda is the population influx rate, beta is the transmission rates, mu is the natural
death rate for the human hosts, gamma is the rate of recovery from the infection, and w is the
disease induced death rate. For simplicity, we set Lambda = mu N, where N is the total population.
For the US, mu = 1/76.33 year−1 = 1/(76.33 ∗ 365) day−1

, and N = 345, 426, 571 people.

Other parameter values are:
COVID-19: gamma = 1/14 day−1

, w = 0.01/7 day−1
;

Flu: gamma = 1/3.5 day−1

, w = 0.006/7 day−1
;

RSV: gamma = 1/10.5 day−1

, w = 0.002/7 day−1
.

Apply this model separately to the US data on COVID-19, Flu, and RSV, and estimate the
value of the transmission rate beta in each case. The data fitting is typically done by using the
least squares method. Based on the fitted beta , calculate the basic reproduction number R0
for each.
I am supposed to consider the above instruction to write matlab codes to find the transmission rate for the flu, covid and the RSV data. Each of these diseases has six seasons starting from 2018-2019 seaons to 2023-2024 seasons.
if the matlab data for 2018-2019 flu season is this:
% Filter for the 2018-19 flu season
Flu_data_2018_2019 = Flu_data(strcmp(Flu_data.Season, ‘2018-19’), :);

% Display the filtered data for the 2018-19 season
disp(Flu_data_2018_2019);

% Display the ‘Weekly_Rate’ column (replace with the correct column name if needed)
Weekly_Rate_2018_2019 = Flu_data_2018_2019.WeeklyRate;
disp(Weekly_Rate_2018_2019);
help write a matlab code to find the transmission rate for the 2018-2019 flu season and the reproduction number An SIR model for COVID-19, Flu, and RSV

We utilize mathematical modeling based on differential equations to investigate the trans-
mission dynamics of the three respiratory infections: COVID-19, Flu, and RSV. To start, let

us consider a simple SIR model, which involves three compartments: the susceptible individ-
uals (denoted by S), the infected individuals (denoted by I), and the recovered individuals

(denoted by R).
The model is described as follows:
dS
dt = Lambda − beta SI − mu S,
dI
dt = beta SI − (gamma + w + mu )I,
dR
dt = gamma I − mu R.

The parameter Lambda is the population influx rate, beta is the transmission rates, mu is the natural
death rate for the human hosts, gamma is the rate of recovery from the infection, and w is the
disease induced death rate. For simplicity, we set Lambda = mu N, where N is the total population.
For the US, mu = 1/76.33 year−1 = 1/(76.33 ∗ 365) day−1

, and N = 345, 426, 571 people.

Other parameter values are:
COVID-19: gamma = 1/14 day−1

, w = 0.01/7 day−1
;

Flu: gamma = 1/3.5 day−1

, w = 0.006/7 day−1
;

RSV: gamma = 1/10.5 day−1

, w = 0.002/7 day−1
.

Apply this model separately to the US data on COVID-19, Flu, and RSV, and estimate the
value of the transmission rate beta in each case. The data fitting is typically done by using the
least squares method. Based on the fitted beta , calculate the basic reproduction number R0
for each.
I am supposed to consider the above instruction to write matlab codes to find the transmission rate for the flu, covid and the RSV data. Each of these diseases has six seasons starting from 2018-2019 seaons to 2023-2024 seasons.
if the matlab data for 2018-2019 flu season is this:
% Filter for the 2018-19 flu season
Flu_data_2018_2019 = Flu_data(strcmp(Flu_data.Season, ‘2018-19’), :);

% Display the filtered data for the 2018-19 season
disp(Flu_data_2018_2019);

% Display the ‘Weekly_Rate’ column (replace with the correct column name if needed)
Weekly_Rate_2018_2019 = Flu_data_2018_2019.WeeklyRate;
disp(Weekly_Rate_2018_2019);
help write a matlab code to find the transmission rate for the 2018-2019 flu season and the reproduction number matlab, least square fitting MATLAB Answers — New Questions

​

Hello,
I wanted to understand what happens to the membership function (in input/output) or rules when I insert in the anfis editor: Sugeno file and a training set.Hello,
I wanted to understand what happens to the membership function (in input/output) or rules when I insert in the anfis editor: Sugeno file and a training set. Hello,
I wanted to understand what happens to the membership function (in input/output) or rules when I insert in the anfis editor: Sugeno file and a training set. sugeno, anfis editor, neuro fuzzy MATLAB Answers — New Questions

​

Hello,I have many plots like this in the data sheet.
there is a java tools which manually allow to extract the data of each plot.
Is the a way in matlab to do it?
Thanks.Hello,I have many plots like this in the data sheet.
there is a java tools which manually allow to extract the data of each plot.
Is the a way in matlab to do it?
Thanks. Hello,I have many plots like this in the data sheet.
there is a java tools which manually allow to extract the data of each plot.
Is the a way in matlab to do it?
Thanks. plot, photo MATLAB Answers — New Questions

​

Hi, I am trying to plots an insets plots in aubplots but only the last subplot and inset plots are shown. Please can someone help with this?

% Define xi_select and slope_select for main and inset plots
xi_select = [1, 10, 11, 12];
slope_select = [1, 3, 4, 5, 7, 9];
% Create a tiled layout with 2 rows and 3 columns of subplots
t = tiledlayout(2, 3, ‘TileSpacing’, ‘compact’, ‘Padding’, ‘compact’);
% Define custom colors (green to red gradient)
num_colors = length(xi_select);
custom_colors = [linspace(0, 1, num_colors)’, linspace(1, 0, num_colors)’, zeros(num_colors, 1)];
custom_linestyles = repmat({‘–‘}, 1, num_colors); % All dashed lines
% Initialize arrays for legend
legend_labels = {}; % Cell array to store legend labels
legend_index = 0; % Initialize legend index
plot_handles = []; % Array for storing plot handles
for j = 1:length(slope_select) % Loop through selected ramp_slope
% Create the main subplot
ax = nexttile; % Move to the next tile for each subplot
hold(ax, ‘on’); % Hold on to plot multiple lines in the same subplot

% Loop through selected xi values for the main plots
for i = 1:length(xi_select)
% Find the switching times for the current xi and ramp_slope
transitions = find(switching(:, xi_select(i), slope_select(j)));
if ~isempty(transitions)
% Plot the uxlt data at the switching times for this xi and ramp_slope
for t_idx = transitions
% Extract the uxlt data for the current time point
uxlt_data = squeeze(uxlt(ym, :, xi_select(i), slope_select(j))); % Dimension: y, xi, ramp_slope

% Proper LaTeX syntax for the legend entry, omitting time
display_name = sprintf(‘$\xi=%.2f$’, xi(xi_select(i)));

% Plot the data with specified color and linestyle
h = plot(ax, tarray, uxlt_data, ‘LineWidth’, 1.5, …
‘Color’, custom_colors(i, :), ‘LineStyle’, custom_linestyles{i});

% Only add the label and handle once to the legend
if ~ismember(display_name, legend_labels)
legend_index = legend_index + 1;
plot_handles(legend_index) = h(1); % Store only the first handle
legend_labels{legend_index} = display_name; % Append label
end
end
end
end

% Add labels and title to each subplot
xlabel(ax, ‘Time (s)’, ‘Interpreter’, ‘latex’);
ylabel(ax, ‘$int u_L , mathrm{dz}$’, ‘Interpreter’, ‘latex’);
xlim(ax, [min(tarray), max(tarray)]);
ylim(ax, [0, 2.5e-04]);

% Add subplot numbering and S_{rf} value
title(ax, sprintf(‘(%c) $S_{rf} = %.2f$’, ‘a’ + j – 1, ramp_slope(slope_select(j))), ‘Interpreter’, ‘latex’);

box(ax, ‘on’); % Place a box around the current subplot
% Add inset plots
inset_pos = ax.Position; % Get the position of the current subplot

if j == 1
% The first inset plot on the top left of the first subplot
inset_pos = [inset_pos(1) + 0.1 * inset_pos(3), inset_pos(2) + 0.65 * inset_pos(4), 0.15, 0.15];
else
% Remaining inset plots on the top right of the respective subplots
inset_pos = [inset_pos(1) + 0.6 * inset_pos(3), inset_pos(2) + 0.65 * inset_pos(4), 0.15, 0.15];
end

% Create new axes for inset plots, without attaching them to the main subplot axes
inset_ax = axes(‘Position’, inset_pos); % Create inset axes with adjusted position
hold(inset_ax, ‘on’); % Hold on inset axes to plot multiple lines

% Loop through selected xi values for the inset plots
for i = 1:length(xi_select)
% Find the switching times for the current xi and ramp_slope
transitions = find(switching(:, xi_select(i), slope_select(j)));
if ~isempty(transitions)
% Plot the uxl data in the inset plot
for t_idx = transitions
% Extract the uxl data for the current time point
uxl_data = squeeze(uxl(:, xi_select(i), slope_select(j))); % Dimension: y, xi, ramp_slope

% Plot the data in the inset with color and linestyle
plot(inset_ax, y, uxl_data, ‘LineWidth’, 1.5, …
‘Color’, custom_colors(i, :), ‘LineStyle’, custom_linestyles{i});
end
end
end

% Add labels to inset
xlabel(inset_ax, ‘$z$’, ‘Interpreter’, ‘latex’);
ylabel(inset_ax, ‘$int u_L , mathrm{dz}$’, ‘Interpreter’, ‘latex’);
xlim(inset_ax, [min(y), max(y)]);
hold(inset_ax, ‘off’); % Release hold on the inset axes
end
% Create a global legend (optional, if needed)
lgd = legend(plot_handles, legend_labels, ‘Interpreter’, ‘latex’, ‘NumColumns’, 2);
% Ensure that the entire figure has a box around it
box on;Hi, I am trying to plots an insets plots in aubplots but only the last subplot and inset plots are shown. Please can someone help with this?

% Define xi_select and slope_select for main and inset plots
xi_select = [1, 10, 11, 12];
slope_select = [1, 3, 4, 5, 7, 9];
% Create a tiled layout with 2 rows and 3 columns of subplots
t = tiledlayout(2, 3, ‘TileSpacing’, ‘compact’, ‘Padding’, ‘compact’);
% Define custom colors (green to red gradient)
num_colors = length(xi_select);
custom_colors = [linspace(0, 1, num_colors)’, linspace(1, 0, num_colors)’, zeros(num_colors, 1)];
custom_linestyles = repmat({‘–‘}, 1, num_colors); % All dashed lines
% Initialize arrays for legend
legend_labels = {}; % Cell array to store legend labels
legend_index = 0; % Initialize legend index
plot_handles = []; % Array for storing plot handles
for j = 1:length(slope_select) % Loop through selected ramp_slope
% Create the main subplot
ax = nexttile; % Move to the next tile for each subplot
hold(ax, ‘on’); % Hold on to plot multiple lines in the same subplot

% Loop through selected xi values for the main plots
for i = 1:length(xi_select)
% Find the switching times for the current xi and ramp_slope
transitions = find(switching(:, xi_select(i), slope_select(j)));
if ~isempty(transitions)
% Plot the uxlt data at the switching times for this xi and ramp_slope
for t_idx = transitions
% Extract the uxlt data for the current time point
uxlt_data = squeeze(uxlt(ym, :, xi_select(i), slope_select(j))); % Dimension: y, xi, ramp_slope

% Proper LaTeX syntax for the legend entry, omitting time
display_name = sprintf(‘$\xi=%.2f$’, xi(xi_select(i)));

% Plot the data with specified color and linestyle
h = plot(ax, tarray, uxlt_data, ‘LineWidth’, 1.5, …
‘Color’, custom_colors(i, :), ‘LineStyle’, custom_linestyles{i});

% Only add the label and handle once to the legend
if ~ismember(display_name, legend_labels)
legend_index = legend_index + 1;
plot_handles(legend_index) = h(1); % Store only the first handle
legend_labels{legend_index} = display_name; % Append label
end
end
end
end

% Add labels and title to each subplot
xlabel(ax, ‘Time (s)’, ‘Interpreter’, ‘latex’);
ylabel(ax, ‘$int u_L , mathrm{dz}$’, ‘Interpreter’, ‘latex’);
xlim(ax, [min(tarray), max(tarray)]);
ylim(ax, [0, 2.5e-04]);

% Add subplot numbering and S_{rf} value
title(ax, sprintf(‘(%c) $S_{rf} = %.2f$’, ‘a’ + j – 1, ramp_slope(slope_select(j))), ‘Interpreter’, ‘latex’);

box(ax, ‘on’); % Place a box around the current subplot
% Add inset plots
inset_pos = ax.Position; % Get the position of the current subplot

if j == 1
% The first inset plot on the top left of the first subplot
inset_pos = [inset_pos(1) + 0.1 * inset_pos(3), inset_pos(2) + 0.65 * inset_pos(4), 0.15, 0.15];
else
% Remaining inset plots on the top right of the respective subplots
inset_pos = [inset_pos(1) + 0.6 * inset_pos(3), inset_pos(2) + 0.65 * inset_pos(4), 0.15, 0.15];
end

% Create new axes for inset plots, without attaching them to the main subplot axes
inset_ax = axes(‘Position’, inset_pos); % Create inset axes with adjusted position
hold(inset_ax, ‘on’); % Hold on inset axes to plot multiple lines

% Loop through selected xi values for the inset plots
for i = 1:length(xi_select)
% Find the switching times for the current xi and ramp_slope
transitions = find(switching(:, xi_select(i), slope_select(j)));
if ~isempty(transitions)
% Plot the uxl data in the inset plot
for t_idx = transitions
% Extract the uxl data for the current time point
uxl_data = squeeze(uxl(:, xi_select(i), slope_select(j))); % Dimension: y, xi, ramp_slope

% Plot the data in the inset with color and linestyle
plot(inset_ax, y, uxl_data, ‘LineWidth’, 1.5, …
‘Color’, custom_colors(i, :), ‘LineStyle’, custom_linestyles{i});
end
end
end

% Add labels to inset
xlabel(inset_ax, ‘$z$’, ‘Interpreter’, ‘latex’);
ylabel(inset_ax, ‘$int u_L , mathrm{dz}$’, ‘Interpreter’, ‘latex’);
xlim(inset_ax, [min(y), max(y)]);
hold(inset_ax, ‘off’); % Release hold on the inset axes
end
% Create a global legend (optional, if needed)
lgd = legend(plot_handles, legend_labels, ‘Interpreter’, ‘latex’, ‘NumColumns’, 2);
% Ensure that the entire figure has a box around it
box on; Hi, I am trying to plots an insets plots in aubplots but only the last subplot and inset plots are shown. Please can someone help with this?

% Define xi_select and slope_select for main and inset plots
xi_select = [1, 10, 11, 12];
slope_select = [1, 3, 4, 5, 7, 9];
% Create a tiled layout with 2 rows and 3 columns of subplots
t = tiledlayout(2, 3, ‘TileSpacing’, ‘compact’, ‘Padding’, ‘compact’);
% Define custom colors (green to red gradient)
num_colors = length(xi_select);
custom_colors = [linspace(0, 1, num_colors)’, linspace(1, 0, num_colors)’, zeros(num_colors, 1)];
custom_linestyles = repmat({‘–‘}, 1, num_colors); % All dashed lines
% Initialize arrays for legend
legend_labels = {}; % Cell array to store legend labels
legend_index = 0; % Initialize legend index
plot_handles = []; % Array for storing plot handles
for j = 1:length(slope_select) % Loop through selected ramp_slope
% Create the main subplot
ax = nexttile; % Move to the next tile for each subplot
hold(ax, ‘on’); % Hold on to plot multiple lines in the same subplot

% Loop through selected xi values for the main plots
for i = 1:length(xi_select)
% Find the switching times for the current xi and ramp_slope
transitions = find(switching(:, xi_select(i), slope_select(j)));
if ~isempty(transitions)
% Plot the uxlt data at the switching times for this xi and ramp_slope
for t_idx = transitions
% Extract the uxlt data for the current time point
uxlt_data = squeeze(uxlt(ym, :, xi_select(i), slope_select(j))); % Dimension: y, xi, ramp_slope

% Proper LaTeX syntax for the legend entry, omitting time
display_name = sprintf(‘$\xi=%.2f$’, xi(xi_select(i)));

% Plot the data with specified color and linestyle
h = plot(ax, tarray, uxlt_data, ‘LineWidth’, 1.5, …
‘Color’, custom_colors(i, :), ‘LineStyle’, custom_linestyles{i});

% Only add the label and handle once to the legend
if ~ismember(display_name, legend_labels)
legend_index = legend_index + 1;
plot_handles(legend_index) = h(1); % Store only the first handle
legend_labels{legend_index} = display_name; % Append label
end
end
end
end

% Add labels and title to each subplot
xlabel(ax, ‘Time (s)’, ‘Interpreter’, ‘latex’);
ylabel(ax, ‘$int u_L , mathrm{dz}$’, ‘Interpreter’, ‘latex’);
xlim(ax, [min(tarray), max(tarray)]);
ylim(ax, [0, 2.5e-04]);

% Add subplot numbering and S_{rf} value
title(ax, sprintf(‘(%c) $S_{rf} = %.2f$’, ‘a’ + j – 1, ramp_slope(slope_select(j))), ‘Interpreter’, ‘latex’);

box(ax, ‘on’); % Place a box around the current subplot
% Add inset plots
inset_pos = ax.Position; % Get the position of the current subplot

if j == 1
% The first inset plot on the top left of the first subplot
inset_pos = [inset_pos(1) + 0.1 * inset_pos(3), inset_pos(2) + 0.65 * inset_pos(4), 0.15, 0.15];
else
% Remaining inset plots on the top right of the respective subplots
inset_pos = [inset_pos(1) + 0.6 * inset_pos(3), inset_pos(2) + 0.65 * inset_pos(4), 0.15, 0.15];
end

% Create new axes for inset plots, without attaching them to the main subplot axes
inset_ax = axes(‘Position’, inset_pos); % Create inset axes with adjusted position
hold(inset_ax, ‘on’); % Hold on inset axes to plot multiple lines

% Loop through selected xi values for the inset plots
for i = 1:length(xi_select)
% Find the switching times for the current xi and ramp_slope
transitions = find(switching(:, xi_select(i), slope_select(j)));
if ~isempty(transitions)
% Plot the uxl data in the inset plot
for t_idx = transitions
% Extract the uxl data for the current time point
uxl_data = squeeze(uxl(:, xi_select(i), slope_select(j))); % Dimension: y, xi, ramp_slope

% Plot the data in the inset with color and linestyle
plot(inset_ax, y, uxl_data, ‘LineWidth’, 1.5, …
‘Color’, custom_colors(i, :), ‘LineStyle’, custom_linestyles{i});
end
end
end

% Add labels to inset
xlabel(inset_ax, ‘$z$’, ‘Interpreter’, ‘latex’);
ylabel(inset_ax, ‘$int u_L , mathrm{dz}$’, ‘Interpreter’, ‘latex’);
xlim(inset_ax, [min(y), max(y)]);
hold(inset_ax, ‘off’); % Release hold on the inset axes
end
% Create a global legend (optional, if needed)
lgd = legend(plot_handles, legend_labels, ‘Interpreter’, ‘latex’, ‘NumColumns’, 2);
% Ensure that the entire figure has a box around it
box on; insets, subplots, figures MATLAB Answers — New Questions

​

Hi, I’m currently using tcpclient command to establish tcpip communication with an external device (not tcpip() from instrument toolbox).
Sometimes, the connection is not stable and when I write data it returns error: ‘An existing connection was forcibly closed by the remote host’.
Therefore, I want to implement a method to check the connection before I write / read from the connection. However, I just couldn’t find much information regarding tcpclient.

I have attempted the following:
% connect
t=tcpclient(‘172.1.1.102′,50000,’Timeout’,1,’ConnectTimeout’,3);
Here, before running the I deliberately disconnected Ethernet cable just want to test trigger the error:
% My intention: try a write / read to check if an error returns
try
write(t,0);
read(t);
disp(‘send succesfull’);
catch ME
disp(‘connection lost’);
disp(ME.identifier);
end
For the first run ‘An existing connection was forcibly closed by the remote host’ still appears in the command window and the ‘send successful’ message is printed, catch statement is skipped. However, a second run will jump into catch statement though.
If I run the try-catch step by step, when it reaches ‘write(t,0)’ statement it returns the ”An existing … remote host”, and continue to ‘read(t)’ statement, and jumps into catch statement.
I couldn’t quite understand why this happened.
Thanks for your help very much!Hi, I’m currently using tcpclient command to establish tcpip communication with an external device (not tcpip() from instrument toolbox).
Sometimes, the connection is not stable and when I write data it returns error: ‘An existing connection was forcibly closed by the remote host’.
Therefore, I want to implement a method to check the connection before I write / read from the connection. However, I just couldn’t find much information regarding tcpclient.

I have attempted the following:
% connect
t=tcpclient(‘172.1.1.102′,50000,’Timeout’,1,’ConnectTimeout’,3);
Here, before running the I deliberately disconnected Ethernet cable just want to test trigger the error:
% My intention: try a write / read to check if an error returns
try
write(t,0);
read(t);
disp(‘send succesfull’);
catch ME
disp(‘connection lost’);
disp(ME.identifier);
end
For the first run ‘An existing connection was forcibly closed by the remote host’ still appears in the command window and the ‘send successful’ message is printed, catch statement is skipped. However, a second run will jump into catch statement though.
If I run the try-catch step by step, when it reaches ‘write(t,0)’ statement it returns the ”An existing … remote host”, and continue to ‘read(t)’ statement, and jumps into catch statement.
I couldn’t quite understand why this happened.
Thanks for your help very much! Hi, I’m currently using tcpclient command to establish tcpip communication with an external device (not tcpip() from instrument toolbox).
Sometimes, the connection is not stable and when I write data it returns error: ‘An existing connection was forcibly closed by the remote host’.
Therefore, I want to implement a method to check the connection before I write / read from the connection. However, I just couldn’t find much information regarding tcpclient.

I have attempted the following:
% connect
t=tcpclient(‘172.1.1.102′,50000,’Timeout’,1,’ConnectTimeout’,3);
Here, before running the I deliberately disconnected Ethernet cable just want to test trigger the error:
% My intention: try a write / read to check if an error returns
try
write(t,0);
read(t);
disp(‘send succesfull’);
catch ME
disp(‘connection lost’);
disp(ME.identifier);
end
For the first run ‘An existing connection was forcibly closed by the remote host’ still appears in the command window and the ‘send successful’ message is printed, catch statement is skipped. However, a second run will jump into catch statement though.
If I run the try-catch step by step, when it reaches ‘write(t,0)’ statement it returns the ”An existing … remote host”, and continue to ‘read(t)’ statement, and jumps into catch statement.
I couldn’t quite understand why this happened.
Thanks for your help very much! matlab, tcpip, tcpclient, connection MATLAB Answers — New Questions

​

Hello,

I have three images and want to store them in a table. I wrote this in command line:

dinfo = dir(‘*.jpg’);
T = table();
for K = 1 : length(dinfo)
filename = dinfo(K).name;
filecontent = imread(filename);
T{filename,1} = filecontent;
end

It gives the error:

To assign to or create a variable in a table, the number of rows must match the height of the table.

Secondly, I want to make my three images equal in size, as we have to make the size of variables in rows equal for tables.

Help needed. Thanks in advance.Hello,

I have three images and want to store them in a table. I wrote this in command line:

dinfo = dir(‘*.jpg’);
T = table();
for K = 1 : length(dinfo)
filename = dinfo(K).name;
filecontent = imread(filename);
T{filename,1} = filecontent;
end

It gives the error:

To assign to or create a variable in a table, the number of rows must match the height of the table.

Secondly, I want to make my three images equal in size, as we have to make the size of variables in rows equal for tables.

Help needed. Thanks in advance. Hello,

I have three images and want to store them in a table. I wrote this in command line:

dinfo = dir(‘*.jpg’);
T = table();
for K = 1 : length(dinfo)
filename = dinfo(K).name;
filecontent = imread(filename);
T{filename,1} = filecontent;
end

It gives the error:

To assign to or create a variable in a table, the number of rows must match the height of the table.

Secondly, I want to make my three images equal in size, as we have to make the size of variables in rows equal for tables.

Help needed. Thanks in advance. matlab tables, table, database MATLAB Answers — New Questions

​

Derivative of state ‘1’ in block ‘FOPIDC/Fractional PID controller1/Fractional derivative1/Transfer
Fcn1′ at time 3.2188803386168852 is not finite. The simulation will be stopped. There may be a
singularity in the solution. If not, try reducing the step size (either by reducing the fixed step
size or by tightening the error tolerances)
how can I solve this errorDerivative of state ‘1’ in block ‘FOPIDC/Fractional PID controller1/Fractional derivative1/Transfer
Fcn1′ at time 3.2188803386168852 is not finite. The simulation will be stopped. There may be a
singularity in the solution. If not, try reducing the step size (either by reducing the fixed step
size or by tightening the error tolerances)
how can I solve this error Derivative of state ‘1’ in block ‘FOPIDC/Fractional PID controller1/Fractional derivative1/Transfer
Fcn1′ at time 3.2188803386168852 is not finite. The simulation will be stopped. There may be a
singularity in the solution. If not, try reducing the step size (either by reducing the fixed step
size or by tightening the error tolerances)
how can I solve this error fopid MATLAB Answers — New Questions

​

Hy all,
my problem concerns the use of input arguments when I define the BytesAvailableFcn of a tcpclient connection.
I create a tcpclient connection:
t = tcpclient(‘address’, port);
And I send a request to the server (which starts automatically to send data):
write( t, ‘request’ );
I want to have a callback function triggered by a bytes available event:
configureCallback( t, "terminator", @MyCallbackFcn );
MyCallbackFcn is written in a separated MyCallbackFcn.m file. The callback function MyCallbackFcn is triggeredd whenever a terminator is available to be read from the remote host specified by the TCP/IP client.
At the moment, MyCallbackFcn uses global parameters (var1, var2, etc.) defined in the main program:
function MyCallbackFcn( src, ~ )
global var1 var2
…
end
This algorithm works well. But in order to avoid the use of global parameters, I would like to define input parameters for MyCallbackFcn fonction. My problems start here, because I do not find the right form to write:
the definition of the callback function in the main program : configureCallback( t,…
the function MyCallbackFcn in the MyCallbackFcn.m file
Thank you in advance for your help.
RaphaëlHy all,
my problem concerns the use of input arguments when I define the BytesAvailableFcn of a tcpclient connection.
I create a tcpclient connection:
t = tcpclient(‘address’, port);
And I send a request to the server (which starts automatically to send data):
write( t, ‘request’ );
I want to have a callback function triggered by a bytes available event:
configureCallback( t, "terminator", @MyCallbackFcn );
MyCallbackFcn is written in a separated MyCallbackFcn.m file. The callback function MyCallbackFcn is triggeredd whenever a terminator is available to be read from the remote host specified by the TCP/IP client.
At the moment, MyCallbackFcn uses global parameters (var1, var2, etc.) defined in the main program:
function MyCallbackFcn( src, ~ )
global var1 var2
…
end
This algorithm works well. But in order to avoid the use of global parameters, I would like to define input parameters for MyCallbackFcn fonction. My problems start here, because I do not find the right form to write:
the definition of the callback function in the main program : configureCallback( t,…
the function MyCallbackFcn in the MyCallbackFcn.m file
Thank you in advance for your help.
Raphaël Hy all,
my problem concerns the use of input arguments when I define the BytesAvailableFcn of a tcpclient connection.
I create a tcpclient connection:
t = tcpclient(‘address’, port);
And I send a request to the server (which starts automatically to send data):
write( t, ‘request’ );
I want to have a callback function triggered by a bytes available event:
configureCallback( t, "terminator", @MyCallbackFcn );
MyCallbackFcn is written in a separated MyCallbackFcn.m file. The callback function MyCallbackFcn is triggeredd whenever a terminator is available to be read from the remote host specified by the TCP/IP client.
At the moment, MyCallbackFcn uses global parameters (var1, var2, etc.) defined in the main program:
function MyCallbackFcn( src, ~ )
global var1 var2
…
end
This algorithm works well. But in order to avoid the use of global parameters, I would like to define input parameters for MyCallbackFcn fonction. My problems start here, because I do not find the right form to write:
the definition of the callback function in the main program : configureCallback( t,…
the function MyCallbackFcn in the MyCallbackFcn.m file
Thank you in advance for your help.
Raphaël tcpclient, bytesavailablefcn, input parameters MATLAB Answers — New Questions

​

I am using this help-yourself
http://www.facstaff.bucknell.edu/maneval/help211/exercises.html

And have a question regarding
2. Let x = [2 5 1 6].
b. Add 3 to just the odd-index elements

They give the answer as

b = x(1:2:end) + 3

but that makes a new matrix with just two numbers in it.

Like so:
"b =

21 20
"

Is the question or the answer wrong?I am using this help-yourself
http://www.facstaff.bucknell.edu/maneval/help211/exercises.html

And have a question regarding
2. Let x = [2 5 1 6].
b. Add 3 to just the odd-index elements

They give the answer as

b = x(1:2:end) + 3

but that makes a new matrix with just two numbers in it.

Like so:
"b =

21 20
"

Is the question or the answer wrong? I am using this help-yourself
http://www.facstaff.bucknell.edu/maneval/help211/exercises.html

And have a question regarding
2. Let x = [2 5 1 6].
b. Add 3 to just the odd-index elements

They give the answer as

b = x(1:2:end) + 3

but that makes a new matrix with just two numbers in it.

Like so:
"b =

21 20
"

Is the question or the answer wrong? odd matrix numbers MATLAB Answers — New Questions

​