Hi,
I have following Figure#1, I want to convert only y-axis to logaritmic scale. I tried several ways but each time outcome is not normal. Can you please tell me how to achive y-axis of Figure#2. Thanks for you help.
% this my figure script.
figure;
imagesc(t, fliplr(f), ST_normalized)
previously I used the follwing command, it did not work. I want to have the image of Figure#2 in y-axis.
for instance, "10" in Figure#1 shoud be "10^1" in a new scaled figure.
set(gca, ‘YScale’, ‘log’)Hi,
I have following Figure#1, I want to convert only y-axis to logaritmic scale. I tried several ways but each time outcome is not normal. Can you please tell me how to achive y-axis of Figure#2. Thanks for you help.
% this my figure script.
figure;
imagesc(t, fliplr(f), ST_normalized)
previously I used the follwing command, it did not work. I want to have the image of Figure#2 in y-axis.
for instance, "10" in Figure#1 shoud be "10^1" in a new scaled figure.
set(gca, ‘YScale’, ‘log’) Hi,
I have following Figure#1, I want to convert only y-axis to logaritmic scale. I tried several ways but each time outcome is not normal. Can you please tell me how to achive y-axis of Figure#2. Thanks for you help.
% this my figure script.
figure;
imagesc(t, fliplr(f), ST_normalized)
previously I used the follwing command, it did not work. I want to have the image of Figure#2 in y-axis.
for instance, "10" in Figure#1 shoud be "10^1" in a new scaled figure.
set(gca, ‘YScale’, ‘log’) matlab, figure, scale MATLAB Answers — New Questions

​

Hi y’all

I am trying to save multiple tables I’ve edited in matlab, but I get this error:

Error using save

Too many output arguments.

I’m having trouble figuring out how to fix this. The code it’s probably referring to is:

function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

from the full code here:

% Pull data from files
function [output] = each_day_table(month, day)
fileNames = sprintf(‘D2024%02d%02d*.csv’, month, day) ;
datastore_result = datastore(fileNames) ;
original_data = readall(datastore_result) ;
output = original_data ;
end

% Find Where Equivilant Diamiter > 150 And Remove (ICB measures b/w 2 – 150 μm)
function [output] = remove_150(original)
new = original ;
Greater150 = original.EquivDiameter > 150 ;
new(Greater150, 🙂 = [] ;
output = new ;
end

% To Save Each Newly Made File Onto Computer
function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

function [output] = do_everything(year, month, day)
original_data = each_day_table(month, day) ;
data_lessthan_150 = remove_150( original_data) ;
save_to_folder = save_on_Computer(data_without_zeroes, month, day) ;
output = save_on_Computer ; % change data_with_surface_area with data_with_ratio
end

OL_06_03 = do_everything (2024,06,03) ;
OL_06_04 = do_everything (2024,06,04) ;
OL_06_05 = do_everything (2024,06,05) ;
OL_06_06 = do_everything (2024,06,06) ;Hi y’all

I am trying to save multiple tables I’ve edited in matlab, but I get this error:

Error using save

Too many output arguments.

I’m having trouble figuring out how to fix this. The code it’s probably referring to is:

function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

from the full code here:

% Pull data from files
function [output] = each_day_table(month, day)
fileNames = sprintf(‘D2024%02d%02d*.csv’, month, day) ;
datastore_result = datastore(fileNames) ;
original_data = readall(datastore_result) ;
output = original_data ;
end

% Find Where Equivilant Diamiter > 150 And Remove (ICB measures b/w 2 – 150 μm)
function [output] = remove_150(original)
new = original ;
Greater150 = original.EquivDiameter > 150 ;
new(Greater150, 🙂 = [] ;
output = new ;
end

% To Save Each Newly Made File Onto Computer
function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

function [output] = do_everything(year, month, day)
original_data = each_day_table(month, day) ;
data_lessthan_150 = remove_150( original_data) ;
save_to_folder = save_on_Computer(data_without_zeroes, month, day) ;
output = save_on_Computer ; % change data_with_surface_area with data_with_ratio
end

OL_06_03 = do_everything (2024,06,03) ;
OL_06_04 = do_everything (2024,06,04) ;
OL_06_05 = do_everything (2024,06,05) ;
OL_06_06 = do_everything (2024,06,06) ; Hi y’all

I am trying to save multiple tables I’ve edited in matlab, but I get this error:

Error using save

Too many output arguments.

I’m having trouble figuring out how to fix this. The code it’s probably referring to is:

function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

from the full code here:

% Pull data from files
function [output] = each_day_table(month, day)
fileNames = sprintf(‘D2024%02d%02d*.csv’, month, day) ;
datastore_result = datastore(fileNames) ;
original_data = readall(datastore_result) ;
output = original_data ;
end

% Find Where Equivilant Diamiter > 150 And Remove (ICB measures b/w 2 – 150 μm)
function [output] = remove_150(original)
new = original ;
Greater150 = original.EquivDiameter > 150 ;
new(Greater150, 🙂 = [] ;
output = new ;
end

% To Save Each Newly Made File Onto Computer
function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

function [output] = do_everything(year, month, day)
original_data = each_day_table(month, day) ;
data_lessthan_150 = remove_150( original_data) ;
save_to_folder = save_on_Computer(data_without_zeroes, month, day) ;
output = save_on_Computer ; % change data_with_surface_area with data_with_ratio
end

OL_06_03 = do_everything (2024,06,03) ;
OL_06_04 = do_everything (2024,06,04) ;
OL_06_05 = do_everything (2024,06,05) ;
OL_06_06 = do_everything (2024,06,06) ; save, error MATLAB Answers — New Questions

​

Is it possible to use the C-Caller in rapid accelerator mode when using buses?
When I include my structs as buses using `Simulink.importExternalCTypes`, I can’t run my simulation in rapid accelerator mode, because there is a name collision between my c code and the code generated by the rapid accelerator build process.
Renaming the structs in my code does not work, as then my c -caller blocks don’t recognize them as valid input/output dataformats.
This problem can be reproduced by using the C-Caller example "Call C Functions Using C Caller Block" (https://de.mathworks.com/help/releases/R2024b/simulink/slref/call-c-functions-using-c-caller-block.html).
When switching the example to rapid accelerator mode, the same problem occurs.Is it possible to use the C-Caller in rapid accelerator mode when using buses?
When I include my structs as buses using `Simulink.importExternalCTypes`, I can’t run my simulation in rapid accelerator mode, because there is a name collision between my c code and the code generated by the rapid accelerator build process.
Renaming the structs in my code does not work, as then my c -caller blocks don’t recognize them as valid input/output dataformats.
This problem can be reproduced by using the C-Caller example "Call C Functions Using C Caller Block" (https://de.mathworks.com/help/releases/R2024b/simulink/slref/call-c-functions-using-c-caller-block.html).
When switching the example to rapid accelerator mode, the same problem occurs. Is it possible to use the C-Caller in rapid accelerator mode when using buses?
When I include my structs as buses using `Simulink.importExternalCTypes`, I can’t run my simulation in rapid accelerator mode, because there is a name collision between my c code and the code generated by the rapid accelerator build process.
Renaming the structs in my code does not work, as then my c -caller blocks don’t recognize them as valid input/output dataformats.
This problem can be reproduced by using the C-Caller example "Call C Functions Using C Caller Block" (https://de.mathworks.com/help/releases/R2024b/simulink/slref/call-c-functions-using-c-caller-block.html).
When switching the example to rapid accelerator mode, the same problem occurs. c-code, c-caller, code-integration, rapid-accelerator, simulink, simulation MATLAB Answers — New Questions

​

I have imported a Creo model via the multibody link to Simscape. When I open the model in Mechanics Explorer, the model itself has been imported correctly, but not all the reference frames are imported (I set reference frames at the desired locations in Creo). I want my reference point at a specific location on my model so that I can measure the position (x, y, z). How can I ensure that all reference frames are imported correctly and set a reference point at a specific location?I have imported a Creo model via the multibody link to Simscape. When I open the model in Mechanics Explorer, the model itself has been imported correctly, but not all the reference frames are imported (I set reference frames at the desired locations in Creo). I want my reference point at a specific location on my model so that I can measure the position (x, y, z). How can I ensure that all reference frames are imported correctly and set a reference point at a specific location? I have imported a Creo model via the multibody link to Simscape. When I open the model in Mechanics Explorer, the model itself has been imported correctly, but not all the reference frames are imported (I set reference frames at the desired locations in Creo). I want my reference point at a specific location on my model so that I can measure the position (x, y, z). How can I ensure that all reference frames are imported correctly and set a reference point at a specific location? multibody link MATLAB Answers — New Questions

​

I have a class which defines an array and then loops over this array updating each element in turn. If I change the length of the array linearly the runtime increases non-linearly. Why? I must be missing something silly.
classdef C
properties
N uint32 {mustBeInteger, mustBePositive}
x (1, 🙂 {mustBeFloat, mustBeNonnegative} = []
end

methods
function c = C(N)
arguments
N uint32 {mustBeInteger, mustBePositive}
end
c.N = N;
c.x = zeros(1, c.N);
end

function run(c)
arguments
c C
end
for i = 1:c.N
c.x(i) = 1;
end
end
end
end

N_range = 1e4:1e4:1e5;
times = zeros(1, length(N_range));
N_i = 1;
for N = N_range
c = C(N);
tic
c.run();
times(N_i) = toc;
N_i = N_i + 1;
end
plot(times)I have a class which defines an array and then loops over this array updating each element in turn. If I change the length of the array linearly the runtime increases non-linearly. Why? I must be missing something silly.
classdef C
properties
N uint32 {mustBeInteger, mustBePositive}
x (1, 🙂 {mustBeFloat, mustBeNonnegative} = []
end

methods
function c = C(N)
arguments
N uint32 {mustBeInteger, mustBePositive}
end
c.N = N;
c.x = zeros(1, c.N);
end

function run(c)
arguments
c C
end
for i = 1:c.N
c.x(i) = 1;
end
end
end
end

N_range = 1e4:1e4:1e5;
times = zeros(1, length(N_range));
N_i = 1;
for N = N_range
c = C(N);
tic
c.run();
times(N_i) = toc;
N_i = N_i + 1;
end
plot(times) I have a class which defines an array and then loops over this array updating each element in turn. If I change the length of the array linearly the runtime increases non-linearly. Why? I must be missing something silly.
classdef C
properties
N uint32 {mustBeInteger, mustBePositive}
x (1, 🙂 {mustBeFloat, mustBeNonnegative} = []
end

methods
function c = C(N)
arguments
N uint32 {mustBeInteger, mustBePositive}
end
c.N = N;
c.x = zeros(1, c.N);
end

function run(c)
arguments
c C
end
for i = 1:c.N
c.x(i) = 1;
end
end
end
end

N_range = 1e4:1e4:1e5;
times = zeros(1, length(N_range));
N_i = 1;
for N = N_range
c = C(N);
tic
c.run();
times(N_i) = toc;
N_i = N_i + 1;
end
plot(times) complexity, validation, overhead, speed, performance MATLAB Answers — New Questions

​

Rotation axis normally defined axis around Z,I want to rotate my modal around x and y axis how can i change rotation axis of the revolute joint?

ThanksRotation axis normally defined axis around Z,I want to rotate my modal around x and y axis how can i change rotation axis of the revolute joint?

Thanks Rotation axis normally defined axis around Z,I want to rotate my modal around x and y axis how can i change rotation axis of the revolute joint?

Thanks rotation, axis, simmechanics MATLAB Answers — New Questions

​

I am trying to create some graphs, and I am struggling to put the legend where I want it in my graph. I would like my graph to look like this:

However, the code that i have wrtiien:
%%% plot mean and SD:
s = sprintf(‘[%c]’, char(176)); % degree angle symbol
figure
title(‘Mean and SD’)
ax1=subplot(4,1,1);
fontsize=get(ax1, ‘FontSize’);
title(‘Start’)
hold on
[m,n] = size(DATA1start);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA1start’,2,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
[m,n] = size(DATA2start);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA2start’,2,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)%stdshade(DATA2start’,’0.3′,’b’)
hold on
[m,n] = size(DATA3start);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA3start’,2,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
hold on
scatter(1:101,mean(DATA1start’,’omitnan’),10,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22")
hold on
scatter(1:101,mean(DATA2start’,’omitnan’),10,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7")
hold on
scatter(1:101,mean(DATA3start’,’omitnan’),10,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09")

ylabel([‘Angular Difference’, s])
xlabel([‘Percentage of gait cycle (%)’])
xlim([0 100])
ylim([0 60])

% Create fake points for the legend
hFakeDATA1 = plot(NaN, NaN, ‘o’, ‘MarkerFaceColor’, "#228b22", ‘MarkerEdgeColor’, "#228b22", ‘MarkerSize’, 5);
hFakeDATA2 = plot(NaN, NaN, ‘o’, ‘MarkerFaceColor’, "#395dc7", ‘MarkerEdgeColor’, "#395dc7", ‘MarkerSize’, 5);
hFakeDATA3 = plot(NaN, NaN, ‘o’, ‘MarkerFaceColor’, "#f88f09", ‘MarkerEdgeColor’, "#f88f09", ‘MarkerSize’, 5);

% Create fake entry for the shaded region
hShadedRegion = plot(NaN, NaN, ‘s’, ‘MarkerFaceColor’, [0.7 0.7 0.7], ‘MarkerEdgeColor’, ‘none’, ‘MarkerSize’, 8);
% Create custom legend with the fake points
legend([hFakeDATA1, hFakeDATA2, hFakeDATA3, hShadedRegion], …
{‘DATA1’, ‘DATA2’, ‘DATA3’, ‘Main effect of group’}, ‘Location’, ‘bestoutside’);

subplot(4,1,2)
title(‘Middle’)
hold on
[m,n] = size(DATA1middle);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA1middle’,2,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
[m,n] = size(DATA2middle);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA2middle’,2,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)%stdshade(DATA2start’,’0.3′,’b’)
hold on
[m,n] = size(DATA3middle);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA3middle’,2,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
hold on
scatter(1:101,mean(DATA1middle’,’omitnan’),10,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22")
hold on
scatter(1:101,mean(DATA2middle’,’omitnan’),10,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7")
hold on
scatter(1:101,mean(DATA3middle’,’omitnan’),10,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09")

ylabel([‘Angular Difference’, s])
xlabel([‘Percentage of gait cycle (%)’])
xlim([0 100])
ylim([0 60])

subplot(4,1,3)
title(‘End’)
hold on
[m,n] = size(DATA1last);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA1last’,2,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
[m,n] = size(DATA2last);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA2last’,2,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)%stdshade(DATA2start’,’0.3′,’b’)
hold on
[m,n] = size(DATA3last);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA3last’,2,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
hold on
scatter(1:101,mean(DATA1last’,’omitnan’),10,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22")
hold on
scatter(1:101,mean(DATA2last’,’omitnan’),10,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7")
hold on
scatter(1:101,mean(DATA3last’,’omitnan’),10,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09")

ylabel([‘Angular Difference’, s])
xlabel([‘Percentage of gait cycle (%)’])
xlim([0 100])
ylim([0 60])
Provides me with the graph shown here. I can’t seem to put the legend in the correct place ( I have moved the block of code around to create the legend before and after each graph, but with no success). I would like the legend to be outside of all the tiles! Any advice would be much appreciated :)I am trying to create some graphs, and I am struggling to put the legend where I want it in my graph. I would like my graph to look like this:

However, the code that i have wrtiien:
%%% plot mean and SD:
s = sprintf(‘[%c]’, char(176)); % degree angle symbol
figure
title(‘Mean and SD’)
ax1=subplot(4,1,1);
fontsize=get(ax1, ‘FontSize’);
title(‘Start’)
hold on
[m,n] = size(DATA1start);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA1start’,2,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
[m,n] = size(DATA2start);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA2start’,2,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)%stdshade(DATA2start’,’0.3′,’b’)
hold on
[m,n] = size(DATA3start);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA3start’,2,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
hold on
scatter(1:101,mean(DATA1start’,’omitnan’),10,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22")
hold on
scatter(1:101,mean(DATA2start’,’omitnan’),10,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7")
hold on
scatter(1:101,mean(DATA3start’,’omitnan’),10,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09")

ylabel([‘Angular Difference’, s])
xlabel([‘Percentage of gait cycle (%)’])
xlim([0 100])
ylim([0 60])

% Create fake points for the legend
hFakeDATA1 = plot(NaN, NaN, ‘o’, ‘MarkerFaceColor’, "#228b22", ‘MarkerEdgeColor’, "#228b22", ‘MarkerSize’, 5);
hFakeDATA2 = plot(NaN, NaN, ‘o’, ‘MarkerFaceColor’, "#395dc7", ‘MarkerEdgeColor’, "#395dc7", ‘MarkerSize’, 5);
hFakeDATA3 = plot(NaN, NaN, ‘o’, ‘MarkerFaceColor’, "#f88f09", ‘MarkerEdgeColor’, "#f88f09", ‘MarkerSize’, 5);

% Create fake entry for the shaded region
hShadedRegion = plot(NaN, NaN, ‘s’, ‘MarkerFaceColor’, [0.7 0.7 0.7], ‘MarkerEdgeColor’, ‘none’, ‘MarkerSize’, 8);
% Create custom legend with the fake points
legend([hFakeDATA1, hFakeDATA2, hFakeDATA3, hShadedRegion], …
{‘DATA1’, ‘DATA2’, ‘DATA3’, ‘Main effect of group’}, ‘Location’, ‘bestoutside’);

subplot(4,1,2)
title(‘Middle’)
hold on
[m,n] = size(DATA1middle);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA1middle’,2,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
[m,n] = size(DATA2middle);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA2middle’,2,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)%stdshade(DATA2start’,’0.3′,’b’)
hold on
[m,n] = size(DATA3middle);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA3middle’,2,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
hold on
scatter(1:101,mean(DATA1middle’,’omitnan’),10,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22")
hold on
scatter(1:101,mean(DATA2middle’,’omitnan’),10,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7")
hold on
scatter(1:101,mean(DATA3middle’,’omitnan’),10,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09")

ylabel([‘Angular Difference’, s])
xlabel([‘Percentage of gait cycle (%)’])
xlim([0 100])
ylim([0 60])

subplot(4,1,3)
title(‘End’)
hold on
[m,n] = size(DATA1last);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA1last’,2,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
[m,n] = size(DATA2last);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA2last’,2,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)%stdshade(DATA2start’,’0.3′,’b’)
hold on
[m,n] = size(DATA3last);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA3last’,2,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
hold on
scatter(1:101,mean(DATA1last’,’omitnan’),10,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22")
hold on
scatter(1:101,mean(DATA2last’,’omitnan’),10,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7")
hold on
scatter(1:101,mean(DATA3last’,’omitnan’),10,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09")

ylabel([‘Angular Difference’, s])
xlabel([‘Percentage of gait cycle (%)’])
xlim([0 100])
ylim([0 60])
Provides me with the graph shown here. I can’t seem to put the legend in the correct place ( I have moved the block of code around to create the legend before and after each graph, but with no success). I would like the legend to be outside of all the tiles! Any advice would be much appreciated 🙂 I am trying to create some graphs, and I am struggling to put the legend where I want it in my graph. I would like my graph to look like this:

However, the code that i have wrtiien:
%%% plot mean and SD:
s = sprintf(‘[%c]’, char(176)); % degree angle symbol
figure
title(‘Mean and SD’)
ax1=subplot(4,1,1);
fontsize=get(ax1, ‘FontSize’);
title(‘Start’)
hold on
[m,n] = size(DATA1start);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA1start’,2,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
[m,n] = size(DATA2start);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA2start’,2,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)%stdshade(DATA2start’,’0.3′,’b’)
hold on
[m,n] = size(DATA3start);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA3start’,2,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
hold on
scatter(1:101,mean(DATA1start’,’omitnan’),10,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22")
hold on
scatter(1:101,mean(DATA2start’,’omitnan’),10,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7")
hold on
scatter(1:101,mean(DATA3start’,’omitnan’),10,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09")

ylabel([‘Angular Difference’, s])
xlabel([‘Percentage of gait cycle (%)’])
xlim([0 100])
ylim([0 60])

% Create fake points for the legend
hFakeDATA1 = plot(NaN, NaN, ‘o’, ‘MarkerFaceColor’, "#228b22", ‘MarkerEdgeColor’, "#228b22", ‘MarkerSize’, 5);
hFakeDATA2 = plot(NaN, NaN, ‘o’, ‘MarkerFaceColor’, "#395dc7", ‘MarkerEdgeColor’, "#395dc7", ‘MarkerSize’, 5);
hFakeDATA3 = plot(NaN, NaN, ‘o’, ‘MarkerFaceColor’, "#f88f09", ‘MarkerEdgeColor’, "#f88f09", ‘MarkerSize’, 5);

% Create fake entry for the shaded region
hShadedRegion = plot(NaN, NaN, ‘s’, ‘MarkerFaceColor’, [0.7 0.7 0.7], ‘MarkerEdgeColor’, ‘none’, ‘MarkerSize’, 8);
% Create custom legend with the fake points
legend([hFakeDATA1, hFakeDATA2, hFakeDATA3, hShadedRegion], …
{‘DATA1’, ‘DATA2’, ‘DATA3’, ‘Main effect of group’}, ‘Location’, ‘bestoutside’);

subplot(4,1,2)
title(‘Middle’)
hold on
[m,n] = size(DATA1middle);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA1middle’,2,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
[m,n] = size(DATA2middle);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA2middle’,2,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)%stdshade(DATA2start’,’0.3′,’b’)
hold on
[m,n] = size(DATA3middle);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA3middle’,2,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
hold on
scatter(1:101,mean(DATA1middle’,’omitnan’),10,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22")
hold on
scatter(1:101,mean(DATA2middle’,’omitnan’),10,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7")
hold on
scatter(1:101,mean(DATA3middle’,’omitnan’),10,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09")

ylabel([‘Angular Difference’, s])
xlabel([‘Percentage of gait cycle (%)’])
xlim([0 100])
ylim([0 60])

subplot(4,1,3)
title(‘End’)
hold on
[m,n] = size(DATA1last);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA1last’,2,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
[m,n] = size(DATA2last);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA2last’,2,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)%stdshade(DATA2start’,’0.3′,’b’)
hold on
[m,n] = size(DATA3last);
xaxis=repmat(1:101, n, 1);
scatter(xaxis,DATA3last’,2,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09", ‘MarkerFaceAlpha’,.3,’MarkerEdgeAlpha’,.3)
hold on
scatter(1:101,mean(DATA1last’,’omitnan’),10,’MarkerFaceColor’,"#228b22",’MarkerEdgeColor’,"#228b22")
hold on
scatter(1:101,mean(DATA2last’,’omitnan’),10,’MarkerFaceColor’,"#395dc7",’MarkerEdgeColor’,"#395dc7")
hold on
scatter(1:101,mean(DATA3last’,’omitnan’),10,’MarkerFaceColor’,"#f88f09",’MarkerEdgeColor’,"#f88f09")

ylabel([‘Angular Difference’, s])
xlabel([‘Percentage of gait cycle (%)’])
xlim([0 100])
ylim([0 60])
Provides me with the graph shown here. I can’t seem to put the legend in the correct place ( I have moved the block of code around to create the legend before and after each graph, but with no success). I would like the legend to be outside of all the tiles! Any advice would be much appreciated 🙂 scatter, legend, tiledlayout, location, bestoutside MATLAB Answers — New Questions

​

Hi there,
I am currently working on a Matlab code for my task (optimization task), and I tried two different methods;

Method A: the fmincon was used to minimize the objective function in which different algorithms, such as interior-point, sqp, sqp-legacy, interior-point, and active-set, were applied.
Method B: Nonlinear least squares optimization was combined with three algorithms, levenberg-marquardt, and trust-region-reflective.

The best results I got now are (from the Nonlinear least squares optimization + ‘interior-point’):
RMSE for X, Y, Z Direction(mm) : [1.18, 0.16, 1.33] mm; however, I want to reduce it as far as it is possible.
@ A quick guide for the code, and how it works.
1. I do have 3D data sets, and I used some mathematical methods to convert these 3D data into 2D data (Step 3).
2. The goal is to use some inverse method to get the same 3D datasets; therefore, after defining the objective function, I applied different optimization methods to address this task.

Any assistance or alternative approaches would be greatly appreciated.
Thank you in advance!
% @ Payam Samadi (2025.3.15) NLSP (Nonlinear least squares optimization)

% In step 3, the 2D projection data was extracted from the 3D data at
% different projections.

% In step4, the objective function is defined.

% In step 5, the Nonlinear least squares optimization is used to minimize
% the objective function.

% Algorithm:
% 1. ‘levenberg-marquardt’ : [1.21, 0.17, 1.39] mm,
% 2. ‘trust-region-reflective’ : [1.21, 0.17, 1.39] mm,
% 3. ‘interior-point’ : [1.18, 0.16, 1.33] mm

% Note: It is important to carefully consider the initial guesses for
% target locations, as well as the lower bounds (lb) and upper bounds (ub).

tic;
clc;
clear;
close all;

%% Step 1: Load xls file (3D Tumor position)

Load_Data = xlsread(‘Sample 1.xlsx’); % Data include 12 (sec)
% Load_Data = xlsread(‘Sample 2.xlsx’); % Data include 60 (sec)

Time = Load_Data(:,1); % Time (s)
xt=Load_Data(:,2); % xt for targets
yt=Load_Data(:,3); % yt for targets
zt=Load_Data(:,4); % zt for targets

true_T=[xt,yt,zt]; % target locations
[~,numT]=size(true_T); % number of targets

%% Step 2: Define imaging system parameters
SAD = 100; % source-axis distance (cm)
SID = 150; % source-image plane distance (cm)

num_projections = length(Time); % number of different views

% Generate projection angles
alpha = linspace(0, 360, num_projections);
% alpha=30;
theta_rad = deg2rad(alpha); % view angles (radians)

%% Step 3: Projection Model: Compute 2D projections

Xp=zeros(1,num_projections); % allocate array
Yp=zeros(1,num_projections);

for i=1:num_projections
f_theta = (SAD – (true_T(i,1) .* sin(theta_rad(i)) + true_T(i,3) .* cos(theta_rad(i)))) ./ SID;
Xp(1, i) = (true_T(i,1) .* cos(theta_rad(i)) – true_T(i,3) .* sin(theta_rad(i))) ./ f_theta;
Yp(1, i) = true_T(i,2) ./ f_theta;
end

xp = Xp’;
yp = Yp’;

%% Step 4: Define the objective function

% Define the objective function
objFun = @(T) computeProjectionError(T, xp, yp, theta_rad, SAD, SID, num_projections);

%% Step 5: Minimize the objective function using lsqnonlin
% Algorithm:
% 1. ‘levenberg-marquardt’ : [1.21, 0.17, 1.39] mm,
% 2. ‘trust-region-reflective’ : [1.21, 0.17, 1.39] mm,
% 3. ‘interior-point’ : [1.18, 0.16, 1.33] mm

% Define bounds
lb = []; % Lower bounds
ub = []; % Upper bounds

% Define optimization options
options = optimoptions(‘lsqnonlin’, …
‘Algorithm’, ‘interior-point’, …
‘Display’, ‘iter’, …
‘MaxIterations’, 300, …
‘MaxFunctionEvaluations’, 20000, …
‘TolFun’, 1e-10, …
‘TolX’, 1e-10, …
‘StepTolerance’, 1e-8, …
‘FiniteDifferenceStepSize’, 1e-3, …
‘UseParallel’, true);

% Generate a better initial guess
T0 = ones(3, num_projections);

% Run optimization for each projection
estimated_T = zeros(3, num_projections);
for i = 1:num_projections
objFun_i = @(T) computeProjectionError(T, xp(i), yp(i), theta_rad(i), SAD, SID, 1);
estimated_T(:, i) = lsqnonlin(objFun_i, T0(:, i), lb, ub, options);
end

%% Step 6: Calculate RMSE value

% Estimated Tumor Position in X, Y, and Z Direction
Estimate_3D_X = estimated_T(1,:);
Estimate_3D_Y = estimated_T(2,:);
Estimate_3D_Z = estimated_T(3,:);

RMSE_x = Calculate_RMSR (xt,Estimate_3D_X);
RMSE_y = Calculate_RMSR (yt,Estimate_3D_Y);
RMSE_z = Calculate_RMSR (zt,Estimate_3D_Z);

fprintf([‘RMSE for X, Y, Z Direction(mm): ‘ …
‘[%.2f, %.2f, %.2f] mmn’], RMSE_x, RMSE_y, RMSE_z);

%% Step 7: Plot Estimated vs Real Data

Plot_results (Time, xt, yt, zt, Estimate_3D_X, Estimate_3D_Y, Estimate_3D_Z);
toc;

%% Objective function: Computes error between observed and estimated projections
function error = computeProjectionError(T, xp, yp, theta_rad, SAD, SID, num_projections)
for i=1:num_projections
x_est = T(1,:);
y_est = T(2,:);
z_est = T(3,:);

% Compute estimated projection using the perspective transformation
f_theta = (SAD – (x_est(i) .* sin(theta_rad(i)) + z_est(i) .* cos(theta_rad(i))))./ SID;

xp_est(i) = (x_est(i) .* cos(theta_rad(i)) – z_est(i) .* sin(theta_rad(i))) ./ f_theta;
yp_est(i) = y_est(i) ./ f_theta;

end

%% Compute residual error (Method 1)
% Compute the residual error
error_x = xp – xp_est;
error_y = yp – yp_est;

% Return residuals for least squares minimization
error = [error_x; error_y];

end
function RMSE = Calculate_RMSR (True_value,Estimated_value)

% Calculate RMSE
e1 = (True_value(:)-Estimated_value(:)).^2;
RMSE = sqrt(mean(e1,’omitnan’));

end
function Plot_results (Time, xt, yt, zt, Estimate_3D_X, Estimate_3D_Y, Estimate_3D_Z)

figure(‘WindowState’, ‘maximized’)
subplot(3,1,1);
plot(Time, Estimate_3D_X,’r’);
hold on;
plot(Time, xt,’b’);
legend (‘Estimate’, ‘Real’);
title (‘X Dierction’)
xlabel (‘Time (s)’);
ylabel (‘Motion (mm)’)
hold off;

subplot(3,1,2);
plot(Time, Estimate_3D_Y,’r’);
hold on;
plot(Time, yt,’b’);
legend (‘Estimate’, ‘Real’);
title (‘Y Dierction’)
xlabel (‘Time (s)’);
ylabel (‘Motion (mm)’)
hold off;

subplot(3,1,3);
plot(Time, Estimate_3D_Z,’r’);
hold on;
plot(Time, zt,’b’);
legend (‘Estimate’, ‘Real’);
title (‘Z Dierction’)
xlabel (‘Time (s)’);
ylabel (‘Motion (mm)’)
hold off;

endHi there,
I am currently working on a Matlab code for my task (optimization task), and I tried two different methods;

Method A: the fmincon was used to minimize the objective function in which different algorithms, such as interior-point, sqp, sqp-legacy, interior-point, and active-set, were applied.
Method B: Nonlinear least squares optimization was combined with three algorithms, levenberg-marquardt, and trust-region-reflective.

The best results I got now are (from the Nonlinear least squares optimization + ‘interior-point’):
RMSE for X, Y, Z Direction(mm) : [1.18, 0.16, 1.33] mm; however, I want to reduce it as far as it is possible.
@ A quick guide for the code, and how it works.
1. I do have 3D data sets, and I used some mathematical methods to convert these 3D data into 2D data (Step 3).
2. The goal is to use some inverse method to get the same 3D datasets; therefore, after defining the objective function, I applied different optimization methods to address this task.

Any assistance or alternative approaches would be greatly appreciated.
Thank you in advance!
% @ Payam Samadi (2025.3.15) NLSP (Nonlinear least squares optimization)

% In step 3, the 2D projection data was extracted from the 3D data at
% different projections.

% In step4, the objective function is defined.

% In step 5, the Nonlinear least squares optimization is used to minimize
% the objective function.

% Algorithm:
% 1. ‘levenberg-marquardt’ : [1.21, 0.17, 1.39] mm,
% 2. ‘trust-region-reflective’ : [1.21, 0.17, 1.39] mm,
% 3. ‘interior-point’ : [1.18, 0.16, 1.33] mm

% Note: It is important to carefully consider the initial guesses for
% target locations, as well as the lower bounds (lb) and upper bounds (ub).

tic;
clc;
clear;
close all;

%% Step 1: Load xls file (3D Tumor position)

Load_Data = xlsread(‘Sample 1.xlsx’); % Data include 12 (sec)
% Load_Data = xlsread(‘Sample 2.xlsx’); % Data include 60 (sec)

Time = Load_Data(:,1); % Time (s)
xt=Load_Data(:,2); % xt for targets
yt=Load_Data(:,3); % yt for targets
zt=Load_Data(:,4); % zt for targets

true_T=[xt,yt,zt]; % target locations
[~,numT]=size(true_T); % number of targets

%% Step 2: Define imaging system parameters
SAD = 100; % source-axis distance (cm)
SID = 150; % source-image plane distance (cm)

num_projections = length(Time); % number of different views

% Generate projection angles
alpha = linspace(0, 360, num_projections);
% alpha=30;
theta_rad = deg2rad(alpha); % view angles (radians)

%% Step 3: Projection Model: Compute 2D projections

Xp=zeros(1,num_projections); % allocate array
Yp=zeros(1,num_projections);

for i=1:num_projections
f_theta = (SAD – (true_T(i,1) .* sin(theta_rad(i)) + true_T(i,3) .* cos(theta_rad(i)))) ./ SID;
Xp(1, i) = (true_T(i,1) .* cos(theta_rad(i)) – true_T(i,3) .* sin(theta_rad(i))) ./ f_theta;
Yp(1, i) = true_T(i,2) ./ f_theta;
end

xp = Xp’;
yp = Yp’;

%% Step 4: Define the objective function

% Define the objective function
objFun = @(T) computeProjectionError(T, xp, yp, theta_rad, SAD, SID, num_projections);

%% Step 5: Minimize the objective function using lsqnonlin
% Algorithm:
% 1. ‘levenberg-marquardt’ : [1.21, 0.17, 1.39] mm,
% 2. ‘trust-region-reflective’ : [1.21, 0.17, 1.39] mm,
% 3. ‘interior-point’ : [1.18, 0.16, 1.33] mm

% Define bounds
lb = []; % Lower bounds
ub = []; % Upper bounds

% Define optimization options
options = optimoptions(‘lsqnonlin’, …
‘Algorithm’, ‘interior-point’, …
‘Display’, ‘iter’, …
‘MaxIterations’, 300, …
‘MaxFunctionEvaluations’, 20000, …
‘TolFun’, 1e-10, …
‘TolX’, 1e-10, …
‘StepTolerance’, 1e-8, …
‘FiniteDifferenceStepSize’, 1e-3, …
‘UseParallel’, true);

% Generate a better initial guess
T0 = ones(3, num_projections);

% Run optimization for each projection
estimated_T = zeros(3, num_projections);
for i = 1:num_projections
objFun_i = @(T) computeProjectionError(T, xp(i), yp(i), theta_rad(i), SAD, SID, 1);
estimated_T(:, i) = lsqnonlin(objFun_i, T0(:, i), lb, ub, options);
end

%% Step 6: Calculate RMSE value

% Estimated Tumor Position in X, Y, and Z Direction
Estimate_3D_X = estimated_T(1,:);
Estimate_3D_Y = estimated_T(2,:);
Estimate_3D_Z = estimated_T(3,:);

RMSE_x = Calculate_RMSR (xt,Estimate_3D_X);
RMSE_y = Calculate_RMSR (yt,Estimate_3D_Y);
RMSE_z = Calculate_RMSR (zt,Estimate_3D_Z);

fprintf([‘RMSE for X, Y, Z Direction(mm): ‘ …
‘[%.2f, %.2f, %.2f] mmn’], RMSE_x, RMSE_y, RMSE_z);

%% Step 7: Plot Estimated vs Real Data

Plot_results (Time, xt, yt, zt, Estimate_3D_X, Estimate_3D_Y, Estimate_3D_Z);
toc;

%% Objective function: Computes error between observed and estimated projections
function error = computeProjectionError(T, xp, yp, theta_rad, SAD, SID, num_projections)
for i=1:num_projections
x_est = T(1,:);
y_est = T(2,:);
z_est = T(3,:);

% Compute estimated projection using the perspective transformation
f_theta = (SAD – (x_est(i) .* sin(theta_rad(i)) + z_est(i) .* cos(theta_rad(i))))./ SID;

xp_est(i) = (x_est(i) .* cos(theta_rad(i)) – z_est(i) .* sin(theta_rad(i))) ./ f_theta;
yp_est(i) = y_est(i) ./ f_theta;

end

%% Compute residual error (Method 1)
% Compute the residual error
error_x = xp – xp_est;
error_y = yp – yp_est;

% Return residuals for least squares minimization
error = [error_x; error_y];

end
function RMSE = Calculate_RMSR (True_value,Estimated_value)

% Calculate RMSE
e1 = (True_value(:)-Estimated_value(:)).^2;
RMSE = sqrt(mean(e1,’omitnan’));

end
function Plot_results (Time, xt, yt, zt, Estimate_3D_X, Estimate_3D_Y, Estimate_3D_Z)

figure(‘WindowState’, ‘maximized’)
subplot(3,1,1);
plot(Time, Estimate_3D_X,’r’);
hold on;
plot(Time, xt,’b’);
legend (‘Estimate’, ‘Real’);
title (‘X Dierction’)
xlabel (‘Time (s)’);
ylabel (‘Motion (mm)’)
hold off;

subplot(3,1,2);
plot(Time, Estimate_3D_Y,’r’);
hold on;
plot(Time, yt,’b’);
legend (‘Estimate’, ‘Real’);
title (‘Y Dierction’)
xlabel (‘Time (s)’);
ylabel (‘Motion (mm)’)
hold off;

subplot(3,1,3);
plot(Time, Estimate_3D_Z,’r’);
hold on;
plot(Time, zt,’b’);
legend (‘Estimate’, ‘Real’);
title (‘Z Dierction’)
xlabel (‘Time (s)’);
ylabel (‘Motion (mm)’)
hold off;

end Hi there,
I am currently working on a Matlab code for my task (optimization task), and I tried two different methods;

Method A: the fmincon was used to minimize the objective function in which different algorithms, such as interior-point, sqp, sqp-legacy, interior-point, and active-set, were applied.
Method B: Nonlinear least squares optimization was combined with three algorithms, levenberg-marquardt, and trust-region-reflective.

The best results I got now are (from the Nonlinear least squares optimization + ‘interior-point’):
RMSE for X, Y, Z Direction(mm) : [1.18, 0.16, 1.33] mm; however, I want to reduce it as far as it is possible.
@ A quick guide for the code, and how it works.
1. I do have 3D data sets, and I used some mathematical methods to convert these 3D data into 2D data (Step 3).
2. The goal is to use some inverse method to get the same 3D datasets; therefore, after defining the objective function, I applied different optimization methods to address this task.

Any assistance or alternative approaches would be greatly appreciated.
Thank you in advance!
% @ Payam Samadi (2025.3.15) NLSP (Nonlinear least squares optimization)

% In step 3, the 2D projection data was extracted from the 3D data at
% different projections.

% In step4, the objective function is defined.

% In step 5, the Nonlinear least squares optimization is used to minimize
% the objective function.

% Algorithm:
% 1. ‘levenberg-marquardt’ : [1.21, 0.17, 1.39] mm,
% 2. ‘trust-region-reflective’ : [1.21, 0.17, 1.39] mm,
% 3. ‘interior-point’ : [1.18, 0.16, 1.33] mm

% Note: It is important to carefully consider the initial guesses for
% target locations, as well as the lower bounds (lb) and upper bounds (ub).

tic;
clc;
clear;
close all;

%% Step 1: Load xls file (3D Tumor position)

Load_Data = xlsread(‘Sample 1.xlsx’); % Data include 12 (sec)
% Load_Data = xlsread(‘Sample 2.xlsx’); % Data include 60 (sec)

Time = Load_Data(:,1); % Time (s)
xt=Load_Data(:,2); % xt for targets
yt=Load_Data(:,3); % yt for targets
zt=Load_Data(:,4); % zt for targets

true_T=[xt,yt,zt]; % target locations
[~,numT]=size(true_T); % number of targets

%% Step 2: Define imaging system parameters
SAD = 100; % source-axis distance (cm)
SID = 150; % source-image plane distance (cm)

num_projections = length(Time); % number of different views

% Generate projection angles
alpha = linspace(0, 360, num_projections);
% alpha=30;
theta_rad = deg2rad(alpha); % view angles (radians)

%% Step 3: Projection Model: Compute 2D projections

Xp=zeros(1,num_projections); % allocate array
Yp=zeros(1,num_projections);

for i=1:num_projections
f_theta = (SAD – (true_T(i,1) .* sin(theta_rad(i)) + true_T(i,3) .* cos(theta_rad(i)))) ./ SID;
Xp(1, i) = (true_T(i,1) .* cos(theta_rad(i)) – true_T(i,3) .* sin(theta_rad(i))) ./ f_theta;
Yp(1, i) = true_T(i,2) ./ f_theta;
end

xp = Xp’;
yp = Yp’;

%% Step 4: Define the objective function

% Define the objective function
objFun = @(T) computeProjectionError(T, xp, yp, theta_rad, SAD, SID, num_projections);

%% Step 5: Minimize the objective function using lsqnonlin
% Algorithm:
% 1. ‘levenberg-marquardt’ : [1.21, 0.17, 1.39] mm,
% 2. ‘trust-region-reflective’ : [1.21, 0.17, 1.39] mm,
% 3. ‘interior-point’ : [1.18, 0.16, 1.33] mm

% Define bounds
lb = []; % Lower bounds
ub = []; % Upper bounds

% Define optimization options
options = optimoptions(‘lsqnonlin’, …
‘Algorithm’, ‘interior-point’, …
‘Display’, ‘iter’, …
‘MaxIterations’, 300, …
‘MaxFunctionEvaluations’, 20000, …
‘TolFun’, 1e-10, …
‘TolX’, 1e-10, …
‘StepTolerance’, 1e-8, …
‘FiniteDifferenceStepSize’, 1e-3, …
‘UseParallel’, true);

% Generate a better initial guess
T0 = ones(3, num_projections);

% Run optimization for each projection
estimated_T = zeros(3, num_projections);
for i = 1:num_projections
objFun_i = @(T) computeProjectionError(T, xp(i), yp(i), theta_rad(i), SAD, SID, 1);
estimated_T(:, i) = lsqnonlin(objFun_i, T0(:, i), lb, ub, options);
end

%% Step 6: Calculate RMSE value

% Estimated Tumor Position in X, Y, and Z Direction
Estimate_3D_X = estimated_T(1,:);
Estimate_3D_Y = estimated_T(2,:);
Estimate_3D_Z = estimated_T(3,:);

RMSE_x = Calculate_RMSR (xt,Estimate_3D_X);
RMSE_y = Calculate_RMSR (yt,Estimate_3D_Y);
RMSE_z = Calculate_RMSR (zt,Estimate_3D_Z);

fprintf([‘RMSE for X, Y, Z Direction(mm): ‘ …
‘[%.2f, %.2f, %.2f] mmn’], RMSE_x, RMSE_y, RMSE_z);

%% Step 7: Plot Estimated vs Real Data

Plot_results (Time, xt, yt, zt, Estimate_3D_X, Estimate_3D_Y, Estimate_3D_Z);
toc;

%% Objective function: Computes error between observed and estimated projections
function error = computeProjectionError(T, xp, yp, theta_rad, SAD, SID, num_projections)
for i=1:num_projections
x_est = T(1,:);
y_est = T(2,:);
z_est = T(3,:);

% Compute estimated projection using the perspective transformation
f_theta = (SAD – (x_est(i) .* sin(theta_rad(i)) + z_est(i) .* cos(theta_rad(i))))./ SID;

xp_est(i) = (x_est(i) .* cos(theta_rad(i)) – z_est(i) .* sin(theta_rad(i))) ./ f_theta;
yp_est(i) = y_est(i) ./ f_theta;

end

%% Compute residual error (Method 1)
% Compute the residual error
error_x = xp – xp_est;
error_y = yp – yp_est;

% Return residuals for least squares minimization
error = [error_x; error_y];

end
function RMSE = Calculate_RMSR (True_value,Estimated_value)

% Calculate RMSE
e1 = (True_value(:)-Estimated_value(:)).^2;
RMSE = sqrt(mean(e1,’omitnan’));

end
function Plot_results (Time, xt, yt, zt, Estimate_3D_X, Estimate_3D_Y, Estimate_3D_Z)

figure(‘WindowState’, ‘maximized’)
subplot(3,1,1);
plot(Time, Estimate_3D_X,’r’);
hold on;
plot(Time, xt,’b’);
legend (‘Estimate’, ‘Real’);
title (‘X Dierction’)
xlabel (‘Time (s)’);
ylabel (‘Motion (mm)’)
hold off;

subplot(3,1,2);
plot(Time, Estimate_3D_Y,’r’);
hold on;
plot(Time, yt,’b’);
legend (‘Estimate’, ‘Real’);
title (‘Y Dierction’)
xlabel (‘Time (s)’);
ylabel (‘Motion (mm)’)
hold off;

subplot(3,1,3);
plot(Time, Estimate_3D_Z,’r’);
hold on;
plot(Time, zt,’b’);
legend (‘Estimate’, ‘Real’);
title (‘Z Dierction’)
xlabel (‘Time (s)’);
ylabel (‘Motion (mm)’)
hold off;

end optimization, fmincon, nonlinear least squares, objective function MATLAB Answers — New Questions

​

When using GPU Coder, I encountered issues with the following environment configuration:
– **GPU Version**: NVIDIA RTX A2000 (Compute Capability 8.6, Ampere Architecture).
– **MATLAB Version**: R2023b.
– **CUDA Toolkit Version**: 11.8.
– **Visual Studio Version**: 2022 (with "Desktop Development with C++" component installed).
When I test
gpuDevice

ans =

CUDADevice – 属性:

Name: ‘NVIDIA RTX A2000’
Index: 1
ComputeCapability: ‘8.6’
SupportsDouble: 1
GraphicsDriverVersion: ‘572.83’
DriverModel: ‘WDDM’
ToolkitVersion: 11.8000
MaxThreadsPerBlock: 1024
MaxShmemPerBlock: 49152 (49.15 KB)
MaxThreadBlockSize: [1024 1024 64]
MaxGridSize: [2.1475e+09 65535 65535]
SIMDWidth: 32
TotalMemory: 6435504128 (6.44 GB)
AvailableMemory: 5375778816 (5.38 GB)
CachePolicy: ‘balanced’
MultiprocessorCount: 26
ClockRateKHz: 1200000
ComputeMode: ‘Default’
GPUOverlapsTransfers: 1
KernelExecutionTimeout: 1
CanMapHostMemory: 1
DeviceSupported: 1
DeviceAvailable: 1
DeviceSelected: 1

If I test coder.checkGpuInstall
coder.checkGpuInstall:

Compatible GPU : PASSED
CUDA Environment : PASSED
Runtime : PASSED
cuFFT : PASSED
cuSOLVER : PASSED
cuBLAS : PASSED
cuDNN Environment : FAILED (Error generated while determining cuDNN library version ‘Embedding metadata in the MEX file failed.
‘.)
Host Compiler : PASSED
Basic Code Generation : FAILED (GPU code generation failed with the error ’emlc:compilationError’. View report for further information: View report)

ans =

包含以下字段的 struct:

gpu: 1
cuda: 1
cudnn: 0
tensorrt: 0
hostcompiler: 1
basiccodegen: 0
basiccodeexec: 0
deepcodegen: 0
deepcodeexec: 0
tensorrtdatatype: 0
profiling: 0
test mex -setup
mex -setup
MEX 配置为使用 ‘MinGW64 Compiler (C)’ 以进行 C 语言编译。

要选择不同的 C 编译器，请从以下选项中选择一种命令:
MinGW64 Compiler (C) mex -setup:C:Users石海洋AppDataRoamingMathWorksMATLABR2023bmex_C_win64.xml C
Microsoft Visual C++ 2022 (C) mex -setup:’D:Program FilesMathWorksR2023Bbinwin64mexoptsmsvc2022.xml’ C

要选择不同的语言，请从以下选项中选择一种命令:
mex -setup C++
mex -setup FORTRAN

I looked throught the answers about ’emlc:compilationError’ and "Invalid CUDA device", and see the answer in GPU coder :basic Code Generation failed – MATLAB Answers – MATLAB Central, its issue is caused by a MATLAB version that is too low. However, after reviewing the relevant settings, my MATLAB version should support CUDA 11.8 and VS2022. So, what could be the problem?
PS: I installed cuDNN 8.9 (cudnn-windows-x86_64-8.9.7.29_cuda11-archive) and set the system environment variable `NVIDIA_CUDNN` to: `C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.8`. Why does the cuDNN Environment still show as failed here?When using GPU Coder, I encountered issues with the following environment configuration:
– **GPU Version**: NVIDIA RTX A2000 (Compute Capability 8.6, Ampere Architecture).
– **MATLAB Version**: R2023b.
– **CUDA Toolkit Version**: 11.8.
– **Visual Studio Version**: 2022 (with "Desktop Development with C++" component installed).
When I test
gpuDevice

ans =

CUDADevice – 属性:

Name: ‘NVIDIA RTX A2000’
Index: 1
ComputeCapability: ‘8.6’
SupportsDouble: 1
GraphicsDriverVersion: ‘572.83’
DriverModel: ‘WDDM’
ToolkitVersion: 11.8000
MaxThreadsPerBlock: 1024
MaxShmemPerBlock: 49152 (49.15 KB)
MaxThreadBlockSize: [1024 1024 64]
MaxGridSize: [2.1475e+09 65535 65535]
SIMDWidth: 32
TotalMemory: 6435504128 (6.44 GB)
AvailableMemory: 5375778816 (5.38 GB)
CachePolicy: ‘balanced’
MultiprocessorCount: 26
ClockRateKHz: 1200000
ComputeMode: ‘Default’
GPUOverlapsTransfers: 1
KernelExecutionTimeout: 1
CanMapHostMemory: 1
DeviceSupported: 1
DeviceAvailable: 1
DeviceSelected: 1

If I test coder.checkGpuInstall
coder.checkGpuInstall:

Compatible GPU : PASSED
CUDA Environment : PASSED
Runtime : PASSED
cuFFT : PASSED
cuSOLVER : PASSED
cuBLAS : PASSED
cuDNN Environment : FAILED (Error generated while determining cuDNN library version ‘Embedding metadata in the MEX file failed.
‘.)
Host Compiler : PASSED
Basic Code Generation : FAILED (GPU code generation failed with the error ’emlc:compilationError’. View report for further information: View report)

ans =

包含以下字段的 struct:

gpu: 1
cuda: 1
cudnn: 0
tensorrt: 0
hostcompiler: 1
basiccodegen: 0
basiccodeexec: 0
deepcodegen: 0
deepcodeexec: 0
tensorrtdatatype: 0
profiling: 0
test mex -setup
mex -setup
MEX 配置为使用 ‘MinGW64 Compiler (C)’ 以进行 C 语言编译。

要选择不同的 C 编译器，请从以下选项中选择一种命令:
MinGW64 Compiler (C) mex -setup:C:Users石海洋AppDataRoamingMathWorksMATLABR2023bmex_C_win64.xml C
Microsoft Visual C++ 2022 (C) mex -setup:’D:Program FilesMathWorksR2023Bbinwin64mexoptsmsvc2022.xml’ C

要选择不同的语言，请从以下选项中选择一种命令:
mex -setup C++
mex -setup FORTRAN

I looked throught the answers about ’emlc:compilationError’ and "Invalid CUDA device", and see the answer in GPU coder :basic Code Generation failed – MATLAB Answers – MATLAB Central, its issue is caused by a MATLAB version that is too low. However, after reviewing the relevant settings, my MATLAB version should support CUDA 11.8 and VS2022. So, what could be the problem?
PS: I installed cuDNN 8.9 (cudnn-windows-x86_64-8.9.7.29_cuda11-archive) and set the system environment variable `NVIDIA_CUDNN` to: `C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.8`. Why does the cuDNN Environment still show as failed here? When using GPU Coder, I encountered issues with the following environment configuration:
– **GPU Version**: NVIDIA RTX A2000 (Compute Capability 8.6, Ampere Architecture).
– **MATLAB Version**: R2023b.
– **CUDA Toolkit Version**: 11.8.
– **Visual Studio Version**: 2022 (with "Desktop Development with C++" component installed).
When I test
gpuDevice

ans =

CUDADevice – 属性:

Name: ‘NVIDIA RTX A2000’
Index: 1
ComputeCapability: ‘8.6’
SupportsDouble: 1
GraphicsDriverVersion: ‘572.83’
DriverModel: ‘WDDM’
ToolkitVersion: 11.8000
MaxThreadsPerBlock: 1024
MaxShmemPerBlock: 49152 (49.15 KB)
MaxThreadBlockSize: [1024 1024 64]
MaxGridSize: [2.1475e+09 65535 65535]
SIMDWidth: 32
TotalMemory: 6435504128 (6.44 GB)
AvailableMemory: 5375778816 (5.38 GB)
CachePolicy: ‘balanced’
MultiprocessorCount: 26
ClockRateKHz: 1200000
ComputeMode: ‘Default’
GPUOverlapsTransfers: 1
KernelExecutionTimeout: 1
CanMapHostMemory: 1
DeviceSupported: 1
DeviceAvailable: 1
DeviceSelected: 1

If I test coder.checkGpuInstall
coder.checkGpuInstall:

Compatible GPU : PASSED
CUDA Environment : PASSED
Runtime : PASSED
cuFFT : PASSED
cuSOLVER : PASSED
cuBLAS : PASSED
cuDNN Environment : FAILED (Error generated while determining cuDNN library version ‘Embedding metadata in the MEX file failed.
‘.)
Host Compiler : PASSED
Basic Code Generation : FAILED (GPU code generation failed with the error ’emlc:compilationError’. View report for further information: View report)

ans =

包含以下字段的 struct:

gpu: 1
cuda: 1
cudnn: 0
tensorrt: 0
hostcompiler: 1
basiccodegen: 0
basiccodeexec: 0
deepcodegen: 0
deepcodeexec: 0
tensorrtdatatype: 0
profiling: 0
test mex -setup
mex -setup
MEX 配置为使用 ‘MinGW64 Compiler (C)’ 以进行 C 语言编译。

要选择不同的 C 编译器，请从以下选项中选择一种命令:
MinGW64 Compiler (C) mex -setup:C:Users石海洋AppDataRoamingMathWorksMATLABR2023bmex_C_win64.xml C
Microsoft Visual C++ 2022 (C) mex -setup:’D:Program FilesMathWorksR2023Bbinwin64mexoptsmsvc2022.xml’ C

要选择不同的语言，请从以下选项中选择一种命令:
mex -setup C++
mex -setup FORTRAN

I looked throught the answers about ’emlc:compilationError’ and "Invalid CUDA device", and see the answer in GPU coder :basic Code Generation failed – MATLAB Answers – MATLAB Central, its issue is caused by a MATLAB version that is too low. However, after reviewing the relevant settings, my MATLAB version should support CUDA 11.8 and VS2022. So, what could be the problem?
PS: I installed cuDNN 8.9 (cudnn-windows-x86_64-8.9.7.29_cuda11-archive) and set the system environment variable `NVIDIA_CUDNN` to: `C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.8`. Why does the cuDNN Environment still show as failed here? gpu coder, basic code generation MATLAB Answers — New Questions

​

I wanted to check if my university has a license for it login into the account does not seem like it. But surely I am doing something wrong because my mail works with almost everything that has a student plan. How can I check if my university is on the list or if not how can I get my university on itI wanted to check if my university has a license for it login into the account does not seem like it. But surely I am doing something wrong because my mail works with almost everything that has a student plan. How can I check if my university is on the list or if not how can I get my university on it I wanted to check if my university has a license for it login into the account does not seem like it. But surely I am doing something wrong because my mail works with almost everything that has a student plan. How can I check if my university is on the list or if not how can I get my university on it license MATLAB Answers — New Questions

​