Hi everyone! I need to calculate PRx (pressure reactivity index) using ICP (intracranial pressure) and mean ABP (mean arterial blood pressure) data. PRx is known as "pressure reactivity index is calculated as the degree of statistical correlation between the slow wave components of mean arterial pressure (ABP) and intracranial pressure (ICP)". I need to do this in order to calculate a different hypothetical reactivity index. I have PRx data- so my idea was to use the given PRx data and compare it to the calculated PRx data to ensure it is correct.
This is the script to display the PRx data that was given to me:
%PRx_display

% Load PRx data from MAT file
load(‘PRx,data_part1of1’);

PRx_values = measurement_data;

disp(‘PRx Values:’);
disp(PRx_values);

% Plot PRx values
figure;
plot(PRx_values);
xlabel(‘Time’);
ylabel(‘PRx Value’);
title(‘PRx’);
grid on;

% Calculate average PRx
average_PRx = mean(PRx_values);

% Display average PRx
fprintf(‘Average PRx: %.4f\n’, average_PRx);
This is the script I am trying to calculate PRx with:
%PRx_calc

% Load ICP and MAP data
load("ICP,data_part1of1");
ICP_time_vector = round(time_vector); % Assuming time_vector in ICP file
ICP_data = measurement_data;

load("ABP,mean,data_part1of1");
ABP_time_vector = round(time_vector); % Assuming time_vector in ABP file
ABP_data = measurement_data;

% Remove duplicate time points from ICP
[ICP_time_vector, uniqueIdx] = unique(ICP_time_vector);
ICP_data = ICP_data(uniqueIdx);

% Remove duplicate time points from ABP
[ABP_time_vector, uniqueIdx] = unique(ABP_time_vector);
ABP_data = ABP_data(uniqueIdx);

% Ensure consistent dimensions
if length(ICP_time_vector) ~= length(ICP_data)
error(‘ICP_time_vector and ICP_data have different lengths’);
end

if length(ABP_time_vector) ~= length(ABP_data)
error(‘ABP_time_vector and ABP_data have different lengths’);
end

% Interpolating ABP to match ICP time vector
if ~isequal(ICP_time_vector, ABP_time_vector)
ABP_data = interp1(ABP_time_vector, ABP_data, ICP_time_vector, ‘linear’, ‘extrap’);
end

% Combine ICP data and aligned ABP data
ICP = [ICP_time_vector(:), ICP_data(:)]; % Ensure column vectors
ABP = [ICP_time_vector(:), ABP_data(:)]; % ABP_data is now interpolated to match ICP_time_vector

% Parameters
windowSize = 30 * 10; % 30 windows of 10 seconds
stepSize = 60; % Update every 60 seconds
n = length(ICP_time_vector); % Number of data points

% Preallocate PRx array
PRx = NaN(floor((n – windowSize) / stepSize) + 1, 1);

% Compute moving correlation coefficient
for i = 1:stepSize:(n – windowSize + 1)
% Extract current window of data
windowICP = ICP(i:i + windowSize – 1, 2);
windowABP = ABP(i:i + windowSize – 1, 2);

% Calculate correlation coefficient for the current window
R = corrcoef(windowICP, windowABP);

% Store PRx (correlation coefficient of ICP and ABP)
PRx(floor(i / stepSize) + 1) = R(1, 2);

end

% Calculate average PRx ignoring NaN values
averagePRx = sum(PRx, ‘omitnan’) / sum(~isnan(PRx));

% Display average PRx in the command window
fprintf(‘Average PRx: %.4f\n’, averagePRx);

% Plot PRx vs Time in minutes
figure;
plot((0:length(PRx)-1)*stepSize/60, PRx); % Convert to minutes
title(‘PRx vs Time’);
xlabel(‘Time (minutes)’);
ylabel(‘PRx Values’);
I thought the calculation script would work, but it doesn’t. For instance, for a certain set of data, PRx_calc.m gives me an average PRx value of 0.32, while PRx_display.m gives me 0.52. The trend of the plots look the same, but the PRx_calc.m’s for some reason looks shifted down and distorted a little bit. I am looking for help with the way I am using the corrcoef function and general debugging help.
Here are the first 20 values of each type of data: First 20 values of
ICP: 10 10 10 10 11 11 11 11 10 11 10 11 11 11 11 11 10 11 11 11
First 20 values of
ABP: 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
First 20 calculated
PRx values: NaN NaN NaN NaN NaN NaN -0.0838 -0.2111 -0.0586 0.1704 0.4276 0.3193 0.3417 0.4128 0.5488 0.4790 0.5207 0.6822 0.7107 0.5235
Average PRx: 0.3209
The PRx_calc script, which produces the above values, should produce the given PRx values(below).
First 20 PRx Values: 0.6150 0.5996 0.6010 0.6276 0.7499 0.4495 0.4488 0.4492 0.5078 0.7263 0.7290 0.7343 0.7490 0.7561 0.6997 0.4609 0.2567 0.5935 0.7317 0.6666
Average PRx: 0.5247
Please let me know if there is anything else I should add, more data points, graphs, etc. Thank you!Hi everyone! I need to calculate PRx (pressure reactivity index) using ICP (intracranial pressure) and mean ABP (mean arterial blood pressure) data. PRx is known as "pressure reactivity index is calculated as the degree of statistical correlation between the slow wave components of mean arterial pressure (ABP) and intracranial pressure (ICP)". I need to do this in order to calculate a different hypothetical reactivity index. I have PRx data- so my idea was to use the given PRx data and compare it to the calculated PRx data to ensure it is correct.
This is the script to display the PRx data that was given to me:
%PRx_display

% Load PRx data from MAT file
load(‘PRx,data_part1of1’);

PRx_values = measurement_data;

disp(‘PRx Values:’);
disp(PRx_values);

% Plot PRx values
figure;
plot(PRx_values);
xlabel(‘Time’);
ylabel(‘PRx Value’);
title(‘PRx’);
grid on;

% Calculate average PRx
average_PRx = mean(PRx_values);

% Display average PRx
fprintf(‘Average PRx: %.4f\n’, average_PRx);
This is the script I am trying to calculate PRx with:
%PRx_calc

% Load ICP and MAP data
load("ICP,data_part1of1");
ICP_time_vector = round(time_vector); % Assuming time_vector in ICP file
ICP_data = measurement_data;

load("ABP,mean,data_part1of1");
ABP_time_vector = round(time_vector); % Assuming time_vector in ABP file
ABP_data = measurement_data;

% Remove duplicate time points from ICP
[ICP_time_vector, uniqueIdx] = unique(ICP_time_vector);
ICP_data = ICP_data(uniqueIdx);

% Remove duplicate time points from ABP
[ABP_time_vector, uniqueIdx] = unique(ABP_time_vector);
ABP_data = ABP_data(uniqueIdx);

% Ensure consistent dimensions
if length(ICP_time_vector) ~= length(ICP_data)
error(‘ICP_time_vector and ICP_data have different lengths’);
end

if length(ABP_time_vector) ~= length(ABP_data)
error(‘ABP_time_vector and ABP_data have different lengths’);
end

% Interpolating ABP to match ICP time vector
if ~isequal(ICP_time_vector, ABP_time_vector)
ABP_data = interp1(ABP_time_vector, ABP_data, ICP_time_vector, ‘linear’, ‘extrap’);
end

% Combine ICP data and aligned ABP data
ICP = [ICP_time_vector(:), ICP_data(:)]; % Ensure column vectors
ABP = [ICP_time_vector(:), ABP_data(:)]; % ABP_data is now interpolated to match ICP_time_vector

% Parameters
windowSize = 30 * 10; % 30 windows of 10 seconds
stepSize = 60; % Update every 60 seconds
n = length(ICP_time_vector); % Number of data points

% Preallocate PRx array
PRx = NaN(floor((n – windowSize) / stepSize) + 1, 1);

% Compute moving correlation coefficient
for i = 1:stepSize:(n – windowSize + 1)
% Extract current window of data
windowICP = ICP(i:i + windowSize – 1, 2);
windowABP = ABP(i:i + windowSize – 1, 2);

% Calculate correlation coefficient for the current window
R = corrcoef(windowICP, windowABP);

% Store PRx (correlation coefficient of ICP and ABP)
PRx(floor(i / stepSize) + 1) = R(1, 2);

end

% Calculate average PRx ignoring NaN values
averagePRx = sum(PRx, ‘omitnan’) / sum(~isnan(PRx));

% Display average PRx in the command window
fprintf(‘Average PRx: %.4f\n’, averagePRx);

% Plot PRx vs Time in minutes
figure;
plot((0:length(PRx)-1)*stepSize/60, PRx); % Convert to minutes
title(‘PRx vs Time’);
xlabel(‘Time (minutes)’);
ylabel(‘PRx Values’);
I thought the calculation script would work, but it doesn’t. For instance, for a certain set of data, PRx_calc.m gives me an average PRx value of 0.32, while PRx_display.m gives me 0.52. The trend of the plots look the same, but the PRx_calc.m’s for some reason looks shifted down and distorted a little bit. I am looking for help with the way I am using the corrcoef function and general debugging help.
Here are the first 20 values of each type of data: First 20 values of
ICP: 10 10 10 10 11 11 11 11 10 11 10 11 11 11 11 11 10 11 11 11
First 20 values of
ABP: 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
First 20 calculated
PRx values: NaN NaN NaN NaN NaN NaN -0.0838 -0.2111 -0.0586 0.1704 0.4276 0.3193 0.3417 0.4128 0.5488 0.4790 0.5207 0.6822 0.7107 0.5235
Average PRx: 0.3209
The PRx_calc script, which produces the above values, should produce the given PRx values(below).
First 20 PRx Values: 0.6150 0.5996 0.6010 0.6276 0.7499 0.4495 0.4488 0.4492 0.5078 0.7263 0.7290 0.7343 0.7490 0.7561 0.6997 0.4609 0.2567 0.5935 0.7317 0.6666
Average PRx: 0.5247
Please let me know if there is anything else I should add, more data points, graphs, etc. Thank you! Hi everyone! I need to calculate PRx (pressure reactivity index) using ICP (intracranial pressure) and mean ABP (mean arterial blood pressure) data. PRx is known as "pressure reactivity index is calculated as the degree of statistical correlation between the slow wave components of mean arterial pressure (ABP) and intracranial pressure (ICP)". I need to do this in order to calculate a different hypothetical reactivity index. I have PRx data- so my idea was to use the given PRx data and compare it to the calculated PRx data to ensure it is correct.
This is the script to display the PRx data that was given to me:
%PRx_display

% Load PRx data from MAT file
load(‘PRx,data_part1of1’);

PRx_values = measurement_data;

disp(‘PRx Values:’);
disp(PRx_values);

% Plot PRx values
figure;
plot(PRx_values);
xlabel(‘Time’);
ylabel(‘PRx Value’);
title(‘PRx’);
grid on;

% Calculate average PRx
average_PRx = mean(PRx_values);

% Display average PRx
fprintf(‘Average PRx: %.4f\n’, average_PRx);
This is the script I am trying to calculate PRx with:
%PRx_calc

% Load ICP and MAP data
load("ICP,data_part1of1");
ICP_time_vector = round(time_vector); % Assuming time_vector in ICP file
ICP_data = measurement_data;

load("ABP,mean,data_part1of1");
ABP_time_vector = round(time_vector); % Assuming time_vector in ABP file
ABP_data = measurement_data;

% Remove duplicate time points from ICP
[ICP_time_vector, uniqueIdx] = unique(ICP_time_vector);
ICP_data = ICP_data(uniqueIdx);

% Remove duplicate time points from ABP
[ABP_time_vector, uniqueIdx] = unique(ABP_time_vector);
ABP_data = ABP_data(uniqueIdx);

% Ensure consistent dimensions
if length(ICP_time_vector) ~= length(ICP_data)
error(‘ICP_time_vector and ICP_data have different lengths’);
end

if length(ABP_time_vector) ~= length(ABP_data)
error(‘ABP_time_vector and ABP_data have different lengths’);
end

% Interpolating ABP to match ICP time vector
if ~isequal(ICP_time_vector, ABP_time_vector)
ABP_data = interp1(ABP_time_vector, ABP_data, ICP_time_vector, ‘linear’, ‘extrap’);
end

% Combine ICP data and aligned ABP data
ICP = [ICP_time_vector(:), ICP_data(:)]; % Ensure column vectors
ABP = [ICP_time_vector(:), ABP_data(:)]; % ABP_data is now interpolated to match ICP_time_vector

% Parameters
windowSize = 30 * 10; % 30 windows of 10 seconds
stepSize = 60; % Update every 60 seconds
n = length(ICP_time_vector); % Number of data points

% Preallocate PRx array
PRx = NaN(floor((n – windowSize) / stepSize) + 1, 1);

% Compute moving correlation coefficient
for i = 1:stepSize:(n – windowSize + 1)
% Extract current window of data
windowICP = ICP(i:i + windowSize – 1, 2);
windowABP = ABP(i:i + windowSize – 1, 2);

% Calculate correlation coefficient for the current window
R = corrcoef(windowICP, windowABP);

% Store PRx (correlation coefficient of ICP and ABP)
PRx(floor(i / stepSize) + 1) = R(1, 2);

end

% Calculate average PRx ignoring NaN values
averagePRx = sum(PRx, ‘omitnan’) / sum(~isnan(PRx));

% Display average PRx in the command window
fprintf(‘Average PRx: %.4f\n’, averagePRx);

% Plot PRx vs Time in minutes
figure;
plot((0:length(PRx)-1)*stepSize/60, PRx); % Convert to minutes
title(‘PRx vs Time’);
xlabel(‘Time (minutes)’);
ylabel(‘PRx Values’);
I thought the calculation script would work, but it doesn’t. For instance, for a certain set of data, PRx_calc.m gives me an average PRx value of 0.32, while PRx_display.m gives me 0.52. The trend of the plots look the same, but the PRx_calc.m’s for some reason looks shifted down and distorted a little bit. I am looking for help with the way I am using the corrcoef function and general debugging help.
Here are the first 20 values of each type of data: First 20 values of
ICP: 10 10 10 10 11 11 11 11 10 11 10 11 11 11 11 11 10 11 11 11
First 20 values of
ABP: 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
First 20 calculated
PRx values: NaN NaN NaN NaN NaN NaN -0.0838 -0.2111 -0.0586 0.1704 0.4276 0.3193 0.3417 0.4128 0.5488 0.4790 0.5207 0.6822 0.7107 0.5235
Average PRx: 0.3209
The PRx_calc script, which produces the above values, should produce the given PRx values(below).
First 20 PRx Values: 0.6150 0.5996 0.6010 0.6276 0.7499 0.4495 0.4488 0.4492 0.5078 0.7263 0.7290 0.7343 0.7490 0.7561 0.6997 0.4609 0.2567 0.5935 0.7317 0.6666
Average PRx: 0.5247
Please let me know if there is anything else I should add, more data points, graphs, etc. Thank you! corrcoef MATLAB Answers — New Questions

​

Hello, with the following code i’m able to retrieve a single axial slice from this nifti file of a lung CT scan. I would like to know how to be able to save a slice from another view, such as sagittal or coronal in a separate image format such at png or jpg for image processing.
L1 = load_nii(‘lung_029.nii’);
NumSlices = size(L1.img,3)

S = L1.img(:,:,125);
figure
imshow(S)
I’m using the image processing toolbox and tools for nifti and analyze image add ons.Hello, with the following code i’m able to retrieve a single axial slice from this nifti file of a lung CT scan. I would like to know how to be able to save a slice from another view, such as sagittal or coronal in a separate image format such at png or jpg for image processing.
L1 = load_nii(‘lung_029.nii’);
NumSlices = size(L1.img,3)

S = L1.img(:,:,125);
figure
imshow(S)
I’m using the image processing toolbox and tools for nifti and analyze image add ons. Hello, with the following code i’m able to retrieve a single axial slice from this nifti file of a lung CT scan. I would like to know how to be able to save a slice from another view, such as sagittal or coronal in a separate image format such at png or jpg for image processing.
L1 = load_nii(‘lung_029.nii’);
NumSlices = size(L1.img,3)

S = L1.img(:,:,125);
figure
imshow(S)
I’m using the image processing toolbox and tools for nifti and analyze image add ons. nifti, image processing, medical images MATLAB Answers — New Questions

​

Hello everyone,

I have a single line of code, quite simple, thanks to which I get (visually) exactly what I need:

h=figure; [pippo1, pippo2]=contourf(AZ,EL,FF, [0, 0]);

where the input arguments have size 301×301. The plot I get is like this:

As you can see, the outside of the contour is not only the big cyan area, but also some small areas in the white (not filled) polygon. I need the coordinates of the outside polygon (the whole cyan region) but I couldn’t extract them from either "h" or "pippo2". Please note, I DON’T NEED the coordinates of the single contours, because if I extract XDATA and YDATA they lose the information about "the inside" and the "outside". I need to extract, in some way, a single contour that represents the whole cyan area, and another one that represents the complementary (white) one.

Thanks a lot!Hello everyone,

I have a single line of code, quite simple, thanks to which I get (visually) exactly what I need:

h=figure; [pippo1, pippo2]=contourf(AZ,EL,FF, [0, 0]);

where the input arguments have size 301×301. The plot I get is like this:

As you can see, the outside of the contour is not only the big cyan area, but also some small areas in the white (not filled) polygon. I need the coordinates of the outside polygon (the whole cyan region) but I couldn’t extract them from either "h" or "pippo2". Please note, I DON’T NEED the coordinates of the single contours, because if I extract XDATA and YDATA they lose the information about "the inside" and the "outside". I need to extract, in some way, a single contour that represents the whole cyan area, and another one that represents the complementary (white) one.

Thanks a lot! Hello everyone,

I have a single line of code, quite simple, thanks to which I get (visually) exactly what I need:

h=figure; [pippo1, pippo2]=contourf(AZ,EL,FF, [0, 0]);

where the input arguments have size 301×301. The plot I get is like this:

As you can see, the outside of the contour is not only the big cyan area, but also some small areas in the white (not filled) polygon. I need the coordinates of the outside polygon (the whole cyan region) but I couldn’t extract them from either "h" or "pippo2". Please note, I DON’T NEED the coordinates of the single contours, because if I extract XDATA and YDATA they lose the information about "the inside" and the "outside". I need to extract, in some way, a single contour that represents the whole cyan area, and another one that represents the complementary (white) one.

Thanks a lot! outside and inside of contourf MATLAB Answers — New Questions

​

SearchAgents_no = 30;
Max_iter = 100;
for j = 1:12
[lb,ub,dim,fobj] = Get_Functions_cec2022(j,2);
[Best_Pos_PSA,Best_Score_PSA,Convergence_curve_PSA] = PSA(SearchAgents_no,Max_iter,lb,ub,dim,fobj);
endSearchAgents_no = 30;
Max_iter = 100;
for j = 1:12
[lb,ub,dim,fobj] = Get_Functions_cec2022(j,2);
[Best_Pos_PSA,Best_Score_PSA,Convergence_curve_PSA] = PSA(SearchAgents_no,Max_iter,lb,ub,dim,fobj);
end SearchAgents_no = 30;
Max_iter = 100;
for j = 1:12
[lb,ub,dim,fobj] = Get_Functions_cec2022(j,2);
[Best_Pos_PSA,Best_Score_PSA,Convergence_curve_PSA] = PSA(SearchAgents_no,Max_iter,lb,ub,dim,fobj);
end cec2022 MATLAB Answers — New Questions

​

I have a very long and complex fitness function that I want to add even more complexity to, and I’m wondering if I can shortcut it.
The basic idea is that an individual has to choose between two patches to forage in and the patches can now have a varying probability, either high or low, of finding food.
I want the combination of probabilities to be different from each other, that is
p1 = prob of finding food in patch 1 = hi or lo
p2 = prob of finding food in patch 2 = hi or lo
(p1,p2) combinations
30% chance of (hi,lo)
25% chance of (hi,hi)
25% chance of (lo,lo)
20% chance of (lo,hi)
The most straight forward way would just be adding it up
total fitness Fd = 0.3(F1) + 0.25(F2) + 0.25(F3) + 0.2(F4)
My code for fitness is already very long with only one combination of finding food probabilities. Is there a way I could do a for loop with these combinations and add in that probability or do I have to go the long way around?I have a very long and complex fitness function that I want to add even more complexity to, and I’m wondering if I can shortcut it.
The basic idea is that an individual has to choose between two patches to forage in and the patches can now have a varying probability, either high or low, of finding food.
I want the combination of probabilities to be different from each other, that is
p1 = prob of finding food in patch 1 = hi or lo
p2 = prob of finding food in patch 2 = hi or lo
(p1,p2) combinations
30% chance of (hi,lo)
25% chance of (hi,hi)
25% chance of (lo,lo)
20% chance of (lo,hi)
The most straight forward way would just be adding it up
total fitness Fd = 0.3(F1) + 0.25(F2) + 0.25(F3) + 0.2(F4)
My code for fitness is already very long with only one combination of finding food probabilities. Is there a way I could do a for loop with these combinations and add in that probability or do I have to go the long way around? I have a very long and complex fitness function that I want to add even more complexity to, and I’m wondering if I can shortcut it.
The basic idea is that an individual has to choose between two patches to forage in and the patches can now have a varying probability, either high or low, of finding food.
I want the combination of probabilities to be different from each other, that is
p1 = prob of finding food in patch 1 = hi or lo
p2 = prob of finding food in patch 2 = hi or lo
(p1,p2) combinations
30% chance of (hi,lo)
25% chance of (hi,hi)
25% chance of (lo,lo)
20% chance of (lo,hi)
The most straight forward way would just be adding it up
total fitness Fd = 0.3(F1) + 0.25(F2) + 0.25(F3) + 0.2(F4)
My code for fitness is already very long with only one combination of finding food probabilities. Is there a way I could do a for loop with these combinations and add in that probability or do I have to go the long way around? for loop, probabilities MATLAB Answers — New Questions

​

Hi, how can I fill the area within the circle? At the moment Matlab fills the area outside. I would like the outside to be white and the inside to be filled. Thanks.
relamdt=-4:0.1:4;
imlamdt=-4:0.1:4;
[x,y]=meshgrid(relamdt,imlamdt);
axis square;
lamdt=x+i*y;
sig = (1 + lamdt);
v = [1,1];
contourf(x,y,abs(sig),v)Hi, how can I fill the area within the circle? At the moment Matlab fills the area outside. I would like the outside to be white and the inside to be filled. Thanks.
relamdt=-4:0.1:4;
imlamdt=-4:0.1:4;
[x,y]=meshgrid(relamdt,imlamdt);
axis square;
lamdt=x+i*y;
sig = (1 + lamdt);
v = [1,1];
contourf(x,y,abs(sig),v) Hi, how can I fill the area within the circle? At the moment Matlab fills the area outside. I would like the outside to be white and the inside to be filled. Thanks.
relamdt=-4:0.1:4;
imlamdt=-4:0.1:4;
[x,y]=meshgrid(relamdt,imlamdt);
axis square;
lamdt=x+i*y;
sig = (1 + lamdt);
v = [1,1];
contourf(x,y,abs(sig),v) contour, fill MATLAB Answers — New Questions

​

hi everyone!
i have a code i need to write-
i need to write a function that gets from the user amount of money he has and ask him how much money he wants to bet on.
the function needs to check that the value is regional and make sense and afterwards, if the value is good it will stop running, is isnt it will keep running until the user will insert regional value.
so far i wrote this- and its working but i dont know how to make the function keep running till the value is good
could use your help it will be helpful
thanx
function betamount = bet(money)
betamount= input(‘on what amount do you want to bet on?: ‘);
if ~isnumeric(betamount)|| betamount<=0 || betamount>money
disp(‘the betamount is invaild, try again’);
end
endhi everyone!
i have a code i need to write-
i need to write a function that gets from the user amount of money he has and ask him how much money he wants to bet on.
the function needs to check that the value is regional and make sense and afterwards, if the value is good it will stop running, is isnt it will keep running until the user will insert regional value.
so far i wrote this- and its working but i dont know how to make the function keep running till the value is good
could use your help it will be helpful
thanx
function betamount = bet(money)
betamount= input(‘on what amount do you want to bet on?: ‘);
if ~isnumeric(betamount)|| betamount<=0 || betamount>money
disp(‘the betamount is invaild, try again’);
end
end hi everyone!
i have a code i need to write-
i need to write a function that gets from the user amount of money he has and ask him how much money he wants to bet on.
the function needs to check that the value is regional and make sense and afterwards, if the value is good it will stop running, is isnt it will keep running until the user will insert regional value.
so far i wrote this- and its working but i dont know how to make the function keep running till the value is good
could use your help it will be helpful
thanx
function betamount = bet(money)
betamount= input(‘on what amount do you want to bet on?: ‘);
if ~isnumeric(betamount)|| betamount<=0 || betamount>money
disp(‘the betamount is invaild, try again’);
end
end function, functions, loop, if statement, while loop MATLAB Answers — New Questions

​

Dear all,
The code below allowed me to download and plot the coastline of UK. However, I would like to have a better resolution. When i change, in line 13 of the code below, ‘gshhs_c.b.gz’ for ‘gshhs_h.b.gz’ an error appeared (see below). So, How can I have the best coastal resolution of the area I an interested in? please can soemone help me?

Error using checkfilename>validateFilename (line 157)
Function GUNZIP was unable to find file ”gshhs_h.b.gz”.
Error in checkfilename (line 49)
[fullfilename, fid] = validateFilename( …
Error in gunzip>checkFilesURLInput (line 124)
[fullFileName, url] = checkfilename(inputFiles{1}, validExtensions, fcnName, …
Error in gunzip (line 63)
[files, url, urlFilename] = checkFilesURLInput(files, {‘gz’},’FILES’,mfilename);
Error in test (line 13)
files = gunzip(‘gshhs_h.b.gz’, workingFolder);
close all
clear all
clc

% assign the path to your working directory:
cd (‘E:SEEC’);

% add path to TelemacTolls functions (i.e. to read in telemac files into MATLAB):
addpath (‘C:Matlab_downloadm_map1.4f’);

workingFolder = tempdir;

files = gunzip(‘gshhs_c.b.gz’, workingFolder);
filename = files{1};
indexfile = gshhs(filename, ‘createindex’);

latlim = [50.45 56.31];
lonlim = [-8.1 -2.1];
S = gshhs(filename, latlim, lonlim);

delete(filename)
delete(indexfile)

levels = [S.Level];
L1 = S(levels == 1);

figure
axesm(‘mercator’, ‘MapLatLimit’, latlim, ‘MapLonLimit’, lonlim)
gridm; mlabel; plabel
geoshow([L1.Lat], [L1.Lon], ‘Color’, ‘blue’)Dear all,
The code below allowed me to download and plot the coastline of UK. However, I would like to have a better resolution. When i change, in line 13 of the code below, ‘gshhs_c.b.gz’ for ‘gshhs_h.b.gz’ an error appeared (see below). So, How can I have the best coastal resolution of the area I an interested in? please can soemone help me?

Error using checkfilename>validateFilename (line 157)
Function GUNZIP was unable to find file ”gshhs_h.b.gz”.
Error in checkfilename (line 49)
[fullfilename, fid] = validateFilename( …
Error in gunzip>checkFilesURLInput (line 124)
[fullFileName, url] = checkfilename(inputFiles{1}, validExtensions, fcnName, …
Error in gunzip (line 63)
[files, url, urlFilename] = checkFilesURLInput(files, {‘gz’},’FILES’,mfilename);
Error in test (line 13)
files = gunzip(‘gshhs_h.b.gz’, workingFolder);
close all
clear all
clc

% assign the path to your working directory:
cd (‘E:SEEC’);

% add path to TelemacTolls functions (i.e. to read in telemac files into MATLAB):
addpath (‘C:Matlab_downloadm_map1.4f’);

workingFolder = tempdir;

files = gunzip(‘gshhs_c.b.gz’, workingFolder);
filename = files{1};
indexfile = gshhs(filename, ‘createindex’);

latlim = [50.45 56.31];
lonlim = [-8.1 -2.1];
S = gshhs(filename, latlim, lonlim);

delete(filename)
delete(indexfile)

levels = [S.Level];
L1 = S(levels == 1);

figure
axesm(‘mercator’, ‘MapLatLimit’, latlim, ‘MapLonLimit’, lonlim)
gridm; mlabel; plabel
geoshow([L1.Lat], [L1.Lon], ‘Color’, ‘blue’) Dear all,
The code below allowed me to download and plot the coastline of UK. However, I would like to have a better resolution. When i change, in line 13 of the code below, ‘gshhs_c.b.gz’ for ‘gshhs_h.b.gz’ an error appeared (see below). So, How can I have the best coastal resolution of the area I an interested in? please can soemone help me?

Error using checkfilename>validateFilename (line 157)
Function GUNZIP was unable to find file ”gshhs_h.b.gz”.
Error in checkfilename (line 49)
[fullfilename, fid] = validateFilename( …
Error in gunzip>checkFilesURLInput (line 124)
[fullFileName, url] = checkfilename(inputFiles{1}, validExtensions, fcnName, …
Error in gunzip (line 63)
[files, url, urlFilename] = checkFilesURLInput(files, {‘gz’},’FILES’,mfilename);
Error in test (line 13)
files = gunzip(‘gshhs_h.b.gz’, workingFolder);
close all
clear all
clc

% assign the path to your working directory:
cd (‘E:SEEC’);

% add path to TelemacTolls functions (i.e. to read in telemac files into MATLAB):
addpath (‘C:Matlab_downloadm_map1.4f’);

workingFolder = tempdir;

files = gunzip(‘gshhs_c.b.gz’, workingFolder);
filename = files{1};
indexfile = gshhs(filename, ‘createindex’);

latlim = [50.45 56.31];
lonlim = [-8.1 -2.1];
S = gshhs(filename, latlim, lonlim);

delete(filename)
delete(indexfile)

levels = [S.Level];
L1 = S(levels == 1);

figure
axesm(‘mercator’, ‘MapLatLimit’, latlim, ‘MapLonLimit’, lonlim)
gridm; mlabel; plabel
geoshow([L1.Lat], [L1.Lon], ‘Color’, ‘blue’) gshhs, increase reoslution MATLAB Answers — New Questions

​

I have a function "databaseConnectWithEval" that is defined as follows:

function conn = databaseConnectWithEval
conn = eval(‘database("MySQL ODBC","username","password");’);
end
If I call this function from the MATLAB Command Window, it works as expected.

However, if I compile this function into a standalone application, and then run the executable, the following error is thrown:

Undefined function ‘database’ for input arguments of type ‘char’.
If I use the following function, without "eval", then both the function and the compiled executable work as expected.

function conn = databaseConnect
conn = database("MySQL ODBC","username","password");
end
Why is this function inside of "eval" not found by my compiled application?I have a function "databaseConnectWithEval" that is defined as follows:

function conn = databaseConnectWithEval
conn = eval(‘database("MySQL ODBC","username","password");’);
end
If I call this function from the MATLAB Command Window, it works as expected.

However, if I compile this function into a standalone application, and then run the executable, the following error is thrown:

Undefined function ‘database’ for input arguments of type ‘char’.
If I use the following function, without "eval", then both the function and the compiled executable work as expected.

function conn = databaseConnect
conn = database("MySQL ODBC","username","password");
end
Why is this function inside of "eval" not found by my compiled application? I have a function "databaseConnectWithEval" that is defined as follows:

function conn = databaseConnectWithEval
conn = eval(‘database("MySQL ODBC","username","password");’);
end
If I call this function from the MATLAB Command Window, it works as expected.

However, if I compile this function into a standalone application, and then run the executable, the following error is thrown:

Undefined function ‘database’ for input arguments of type ‘char’.
If I use the following function, without "eval", then both the function and the compiled executable work as expected.

function conn = databaseConnect
conn = database("MySQL ODBC","username","password");
end
Why is this function inside of "eval" not found by my compiled application? eval, compiler MATLAB Answers — New Questions

​

When I run the System Validation function I get the following error message in Workspace.

‘Tri 3×3’ is the name of the FIS I’ve designed.
All other simulation functions work fine.When I run the System Validation function I get the following error message in Workspace.

‘Tri 3×3’ is the name of the FIS I’ve designed.
All other simulation functions work fine. When I run the System Validation function I get the following error message in Workspace.

‘Tri 3×3’ is the name of the FIS I’ve designed.
All other simulation functions work fine. fuzzy logic designer app, system validation MATLAB Answers — New Questions

​