Hello everyone, I hope you can help me with the following problem. I have 3 measurement data sets, consisting of x-values (e.g. x1) and 2 corresponding y-data sets (e.g. A1 and B1). The functions of these parameters have partially shared parameters. A simultaneous fit over both curves works so far. Now I want to run a global fit over all 3 measurement datasets simultaneously, where all 3 measurement datasets share one parameter (beta(10)). How do I do this? Previously, I received a value for beta (10) for each fit, but now I want to change this. My data and functions (non-linear) are very extensive, so I have created a simplified example.
b1 = 1; b2 = 0.85; b3 = 2.5;
b4 = 1.1; b5 = 2.2; b6 = 4.5;
b7 = 1.3; b8 = 7.2; b9 = 9.5;
b10 = 0.5;
%x data
x1 = linspace(0, 10, 20).’; x2 = linspace(0, 10, 23).’; x3 = linspace(0, 10, 14).’;
%constants
C1 = 3.7 ; C2 = 4.2; C3 = 20.2;
%measurement dataset 1
A1 = b1 + b2*x1 + C1 * b10 + rand(20,1);
B1 = b3 – b2*x1 + C1 * b10 + rand(20,1);
%measurement dataset 2
A2 = b4 + b5*x2 + C2 * b10 + rand(23,1);
B2 = b6 – b5*x2 + C2 * b10 + rand(23,1);
%measurement dataset 3
A3 = b7 + b8*x3 + C3 * b10 + rand(14,1);
B3 = b9 – b8*x3 + C3 * b10 + rand(14,1);

mdl1 = @(beta, x) [beta(1) + beta(2).*x + C1 .* beta(10) ,…
beta(3) – beta(2).*x + C1 .* beta(10)];
mdl2 = @(beta, x) [beta(4) + beta(5).*x + C2 .* beta(10) ,…
beta(6) – beta(5).*x + C2 .* beta(10)];
mdl3 = @(beta, x) [beta(7) + beta(8).*x + C3 .* beta(10) ,…
beta(9) – beta(8).*x + C3 .* beta(10)];
beta0 = [0.92, 0.8, 2, 0.7, 2, 4, 1, 7, 9, 1];
lb = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
ub = [15, 15, 15, 15, 15, 15, 15, 15, 15, 15];
options = optimoptions(@lsqcurvefit,’Algorithm’,’levenberg-marquardt’);
beta1 = lsqcurvefit(mdl1,beta0,x1,[A1, B1],lb,ub,options);
beta2 = lsqcurvefit(mdl2,beta0,x2,[A2, B2],lb,ub,options);
beta3 = lsqcurvefit(mdl3,beta0,x3,[A3, B3],lb,ub,options);
A1_fit = beta1(1) + beta1(2)*x1 + C1 * beta1(10);
B1_fit = beta1(3) – beta1(2)*x1 + C1 * beta1(10);
A2_fit = beta2(4) + beta2(5)*x2 + C2 * beta2(10);
B2_fit = beta2(6) – beta2(5)*x2 + C2 * beta2(10);
A3_fit = beta3(7) + beta3(8)*x3 + C3 * beta3(10);
B3_fit = beta3(9) – beta3(8)*x3 + C3 * beta3(10);
figure(1);
subplot(2,1,1);
hold on;
plot(x1, A1,’s’);
plot(x1, A1_fit);
plot(x2, A2,’d’);
plot(x2, A2_fit);
plot(x3, A3,’p’);
plot(x3, A3_fit);
subplot(2,1,2);
hold on;
plot(x1, B1,’s’);
plot(x1, B1_fit);
plot(x2, B2,’d’);
plot(x2, B2_fit);
plot(x3, B3,’p’);
plot(x3, B3_fit);
hold offHello everyone, I hope you can help me with the following problem. I have 3 measurement data sets, consisting of x-values (e.g. x1) and 2 corresponding y-data sets (e.g. A1 and B1). The functions of these parameters have partially shared parameters. A simultaneous fit over both curves works so far. Now I want to run a global fit over all 3 measurement datasets simultaneously, where all 3 measurement datasets share one parameter (beta(10)). How do I do this? Previously, I received a value for beta (10) for each fit, but now I want to change this. My data and functions (non-linear) are very extensive, so I have created a simplified example.
b1 = 1; b2 = 0.85; b3 = 2.5;
b4 = 1.1; b5 = 2.2; b6 = 4.5;
b7 = 1.3; b8 = 7.2; b9 = 9.5;
b10 = 0.5;
%x data
x1 = linspace(0, 10, 20).’; x2 = linspace(0, 10, 23).’; x3 = linspace(0, 10, 14).’;
%constants
C1 = 3.7 ; C2 = 4.2; C3 = 20.2;
%measurement dataset 1
A1 = b1 + b2*x1 + C1 * b10 + rand(20,1);
B1 = b3 – b2*x1 + C1 * b10 + rand(20,1);
%measurement dataset 2
A2 = b4 + b5*x2 + C2 * b10 + rand(23,1);
B2 = b6 – b5*x2 + C2 * b10 + rand(23,1);
%measurement dataset 3
A3 = b7 + b8*x3 + C3 * b10 + rand(14,1);
B3 = b9 – b8*x3 + C3 * b10 + rand(14,1);

mdl1 = @(beta, x) [beta(1) + beta(2).*x + C1 .* beta(10) ,…
beta(3) – beta(2).*x + C1 .* beta(10)];
mdl2 = @(beta, x) [beta(4) + beta(5).*x + C2 .* beta(10) ,…
beta(6) – beta(5).*x + C2 .* beta(10)];
mdl3 = @(beta, x) [beta(7) + beta(8).*x + C3 .* beta(10) ,…
beta(9) – beta(8).*x + C3 .* beta(10)];
beta0 = [0.92, 0.8, 2, 0.7, 2, 4, 1, 7, 9, 1];
lb = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
ub = [15, 15, 15, 15, 15, 15, 15, 15, 15, 15];
options = optimoptions(@lsqcurvefit,’Algorithm’,’levenberg-marquardt’);
beta1 = lsqcurvefit(mdl1,beta0,x1,[A1, B1],lb,ub,options);
beta2 = lsqcurvefit(mdl2,beta0,x2,[A2, B2],lb,ub,options);
beta3 = lsqcurvefit(mdl3,beta0,x3,[A3, B3],lb,ub,options);
A1_fit = beta1(1) + beta1(2)*x1 + C1 * beta1(10);
B1_fit = beta1(3) – beta1(2)*x1 + C1 * beta1(10);
A2_fit = beta2(4) + beta2(5)*x2 + C2 * beta2(10);
B2_fit = beta2(6) – beta2(5)*x2 + C2 * beta2(10);
A3_fit = beta3(7) + beta3(8)*x3 + C3 * beta3(10);
B3_fit = beta3(9) – beta3(8)*x3 + C3 * beta3(10);
figure(1);
subplot(2,1,1);
hold on;
plot(x1, A1,’s’);
plot(x1, A1_fit);
plot(x2, A2,’d’);
plot(x2, A2_fit);
plot(x3, A3,’p’);
plot(x3, A3_fit);
subplot(2,1,2);
hold on;
plot(x1, B1,’s’);
plot(x1, B1_fit);
plot(x2, B2,’d’);
plot(x2, B2_fit);
plot(x3, B3,’p’);
plot(x3, B3_fit);
hold off Hello everyone, I hope you can help me with the following problem. I have 3 measurement data sets, consisting of x-values (e.g. x1) and 2 corresponding y-data sets (e.g. A1 and B1). The functions of these parameters have partially shared parameters. A simultaneous fit over both curves works so far. Now I want to run a global fit over all 3 measurement datasets simultaneously, where all 3 measurement datasets share one parameter (beta(10)). How do I do this? Previously, I received a value for beta (10) for each fit, but now I want to change this. My data and functions (non-linear) are very extensive, so I have created a simplified example.
b1 = 1; b2 = 0.85; b3 = 2.5;
b4 = 1.1; b5 = 2.2; b6 = 4.5;
b7 = 1.3; b8 = 7.2; b9 = 9.5;
b10 = 0.5;
%x data
x1 = linspace(0, 10, 20).’; x2 = linspace(0, 10, 23).’; x3 = linspace(0, 10, 14).’;
%constants
C1 = 3.7 ; C2 = 4.2; C3 = 20.2;
%measurement dataset 1
A1 = b1 + b2*x1 + C1 * b10 + rand(20,1);
B1 = b3 – b2*x1 + C1 * b10 + rand(20,1);
%measurement dataset 2
A2 = b4 + b5*x2 + C2 * b10 + rand(23,1);
B2 = b6 – b5*x2 + C2 * b10 + rand(23,1);
%measurement dataset 3
A3 = b7 + b8*x3 + C3 * b10 + rand(14,1);
B3 = b9 – b8*x3 + C3 * b10 + rand(14,1);

mdl1 = @(beta, x) [beta(1) + beta(2).*x + C1 .* beta(10) ,…
beta(3) – beta(2).*x + C1 .* beta(10)];
mdl2 = @(beta, x) [beta(4) + beta(5).*x + C2 .* beta(10) ,…
beta(6) – beta(5).*x + C2 .* beta(10)];
mdl3 = @(beta, x) [beta(7) + beta(8).*x + C3 .* beta(10) ,…
beta(9) – beta(8).*x + C3 .* beta(10)];
beta0 = [0.92, 0.8, 2, 0.7, 2, 4, 1, 7, 9, 1];
lb = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
ub = [15, 15, 15, 15, 15, 15, 15, 15, 15, 15];
options = optimoptions(@lsqcurvefit,’Algorithm’,’levenberg-marquardt’);
beta1 = lsqcurvefit(mdl1,beta0,x1,[A1, B1],lb,ub,options);
beta2 = lsqcurvefit(mdl2,beta0,x2,[A2, B2],lb,ub,options);
beta3 = lsqcurvefit(mdl3,beta0,x3,[A3, B3],lb,ub,options);
A1_fit = beta1(1) + beta1(2)*x1 + C1 * beta1(10);
B1_fit = beta1(3) – beta1(2)*x1 + C1 * beta1(10);
A2_fit = beta2(4) + beta2(5)*x2 + C2 * beta2(10);
B2_fit = beta2(6) – beta2(5)*x2 + C2 * beta2(10);
A3_fit = beta3(7) + beta3(8)*x3 + C3 * beta3(10);
B3_fit = beta3(9) – beta3(8)*x3 + C3 * beta3(10);
figure(1);
subplot(2,1,1);
hold on;
plot(x1, A1,’s’);
plot(x1, A1_fit);
plot(x2, A2,’d’);
plot(x2, A2_fit);
plot(x3, A3,’p’);
plot(x3, A3_fit);
subplot(2,1,2);
hold on;
plot(x1, B1,’s’);
plot(x1, B1_fit);
plot(x2, B2,’d’);
plot(x2, B2_fit);
plot(x3, B3,’p’);
plot(x3, B3_fit);
hold off lsqcurvefit, fitting multiple curves MATLAB Answers — New Questions

​

Hello MATLAB Community,
I am currently working on a Simscape model where a battery is connected to a current limiter and subsequently to a "motor and drive" system block. I am experiencing an issue where the simulation crashes as soon as the current limiter attempts to limit the current.
Error Details:
Error:An error occurred during simulation and the simulation was terminated
Caused by:
[‘Model_Name/Solver’]: Transient initialization at time 0.04421663720467431, solving for consistent states and modes, failed to converge.
Nonlinear solver: failed to converge, residual norm too large.
Here is the set of components with unconverged equations:
‘Model_Name/Current Limiter’
Equation location is:
‘/Applications/MATLAB_R2024a.app/toolbox/physmod/elec/library/m/+ee/+semiconductors/current_limiter_base.sscp'(no line number info)

What I Have Tried
Solver Configuration: I have tried using stiff solvers such as ode15s and ode23t, and adjusted the tolerances.
Initialization: Ensured that all initial conditions are set correctly for the battery, current limiter, and motor system.
Current Limiter Settings: Reviewed and adjusted the current limiter parameters to avoid abrupt changes.
Simulation Step Size: Reduced the step size to capture transient behaviors more accurately.
Zero-Crossing Detection: Enabled zero-crossing detection to handle state changes more smoothly.
Component Parameters: Verified that all component parameters are realistic and consistent.
Protective Measures: Tried introducing capacitors and resistors to smooth out voltage drops and dampen sudden changes.
Despite these efforts, the simulation still crashes. I believe the issue might be related to the current limiter’s interaction with the motor and drive system.
Reference Model
I am using the model configuration similar to the one available here: Battery Electric Vehicle with Motor Cooling in Simscape.
Request for Assistance
I am seeking advice on how to effectively limit the current to the motor and drive system without causing the simulation to crash. Any insights or suggestions on how to resolve this issue would be greatly appreciated.
Thank you in advance for your help!
Best regards, AlexHello MATLAB Community,
I am currently working on a Simscape model where a battery is connected to a current limiter and subsequently to a "motor and drive" system block. I am experiencing an issue where the simulation crashes as soon as the current limiter attempts to limit the current.
Error Details:
Error:An error occurred during simulation and the simulation was terminated
Caused by:
[‘Model_Name/Solver’]: Transient initialization at time 0.04421663720467431, solving for consistent states and modes, failed to converge.
Nonlinear solver: failed to converge, residual norm too large.
Here is the set of components with unconverged equations:
‘Model_Name/Current Limiter’
Equation location is:
‘/Applications/MATLAB_R2024a.app/toolbox/physmod/elec/library/m/+ee/+semiconductors/current_limiter_base.sscp'(no line number info)

What I Have Tried
Solver Configuration: I have tried using stiff solvers such as ode15s and ode23t, and adjusted the tolerances.
Initialization: Ensured that all initial conditions are set correctly for the battery, current limiter, and motor system.
Current Limiter Settings: Reviewed and adjusted the current limiter parameters to avoid abrupt changes.
Simulation Step Size: Reduced the step size to capture transient behaviors more accurately.
Zero-Crossing Detection: Enabled zero-crossing detection to handle state changes more smoothly.
Component Parameters: Verified that all component parameters are realistic and consistent.
Protective Measures: Tried introducing capacitors and resistors to smooth out voltage drops and dampen sudden changes.
Despite these efforts, the simulation still crashes. I believe the issue might be related to the current limiter’s interaction with the motor and drive system.
Reference Model
I am using the model configuration similar to the one available here: Battery Electric Vehicle with Motor Cooling in Simscape.
Request for Assistance
I am seeking advice on how to effectively limit the current to the motor and drive system without causing the simulation to crash. Any insights or suggestions on how to resolve this issue would be greatly appreciated.
Thank you in advance for your help!
Best regards, Alex Hello MATLAB Community,
I am currently working on a Simscape model where a battery is connected to a current limiter and subsequently to a "motor and drive" system block. I am experiencing an issue where the simulation crashes as soon as the current limiter attempts to limit the current.
Error Details:
Error:An error occurred during simulation and the simulation was terminated
Caused by:
[‘Model_Name/Solver’]: Transient initialization at time 0.04421663720467431, solving for consistent states and modes, failed to converge.
Nonlinear solver: failed to converge, residual norm too large.
Here is the set of components with unconverged equations:
‘Model_Name/Current Limiter’
Equation location is:
‘/Applications/MATLAB_R2024a.app/toolbox/physmod/elec/library/m/+ee/+semiconductors/current_limiter_base.sscp'(no line number info)

What I Have Tried
Solver Configuration: I have tried using stiff solvers such as ode15s and ode23t, and adjusted the tolerances.
Initialization: Ensured that all initial conditions are set correctly for the battery, current limiter, and motor system.
Current Limiter Settings: Reviewed and adjusted the current limiter parameters to avoid abrupt changes.
Simulation Step Size: Reduced the step size to capture transient behaviors more accurately.
Zero-Crossing Detection: Enabled zero-crossing detection to handle state changes more smoothly.
Component Parameters: Verified that all component parameters are realistic and consistent.
Protective Measures: Tried introducing capacitors and resistors to smooth out voltage drops and dampen sudden changes.
Despite these efforts, the simulation still crashes. I believe the issue might be related to the current limiter’s interaction with the motor and drive system.
Reference Model
I am using the model configuration similar to the one available here: Battery Electric Vehicle with Motor Cooling in Simscape.
Request for Assistance
I am seeking advice on how to effectively limit the current to the motor and drive system without causing the simulation to crash. Any insights or suggestions on how to resolve this issue would be greatly appreciated.
Thank you in advance for your help!
Best regards, Alex simscape, battery_system_management MATLAB Answers — New Questions

​

Currently, I am facing three issues related to 3D reconstruction. As shown in the first attached image, I describe the x-axis as positive to the right and the y-axis as positive upwards (since the checkerboard is planar, I will not consider the z-axis in this question). The green plots mean the ground truth, and the red plots mean reconstructed values in the first attached image.
When considering the detected checkerboard corner points in the same x×y layout as the premise, the ground truth is detected and plotted as 8×11, while the reconstructed values are detected and plotted as 10×7. Why is there a difference of one cell in the detected grid? Please refer to the attached checkerboard image for this situation.
As seen in the attached image, the long side of the ground truth checkerboard is aligned along the y-axis direction, while the long side of the reconstructed checkerboard appears to be aligned along the x-axis direction. Why does it seem like the long side’s position has rotated during reconstruction?
The first detected corner point of the reconstructed values should be located at the same origin as the first detected corner point of the ground truth, but it is located in a different position. What is the cause of this? Is this due to low accuracy in the camera parameters, or is it because the reconstructed values and the ground truth are represented in different coordinate systems? Could there be other reasons for this?
The specific causes for the second and third questions might be the same, but I appreciate your response. For reference, I am attaching the code as well.

% Loading stereo images
I1 = imread(‘/Users/uchidataisei/dev/uchida/kenkyu_data/2024-06-14/C3/GX010541_frames/frame0052.png’); % 左画像
I2 = imread(‘/Users/uchidataisei/dev/uchida/kenkyu_data/2024-06-14/C2/GX010611_frames/frame0052.png’); % 右画像

% Using pre-obtained calibration parameters
load(‘/Users/uchidataisei/dev/uchida/MATLAB_data/2024_06_14/calibrationSession_0614_C3_2_.mat’);

% Rectification
% [J1, J2, reprojectionMatrix] = rectifyStereoImages(I1, I2, stereoParams_0614_C3_2);
[J1, J2, reprojectionMatrix] = rectifyStereoImages(I1, I2, stereoParams_0614_C3_2_);

% Calculating the disparity map
disparityMap = disparitySGM(rgb2gray(J1), rgb2gray(J2));

% Reconstructing the 3D scene
points3D = reconstructScene(disparityMap, reprojectionMatrix);

% Setting the ground truth of the checkerboard, [mm]
squareSize = 114;

% Detecting the corners of the checkerboard
[imagePoints, boardSize] = detectCheckerboardPoints(J1);

% Calculating the ground truth grid of the checkerboard
[worldX, worldY] = meshgrid(0:squareSize:((boardSize(1)-1)*squareSize), 0:squareSize:((boardSize(2)-1)*squareSize));
worldPoints = [worldX(:), worldY(:), zeros(numel(worldX), 1)]; % Z=0

% Matching the number of detected corners with the number of ground truth points
% if size(worldPoints, 1) > size(imagePoints, 1)
% worldPoints = worldPoints(1:size(imagePoints, 1), :);
% elseif size(worldPoints, 1) < size(imagePoints, 1)
% error(‘The number of ground truth points is less than the number of detected corners. Please check the number of ground truth points.’);
% end

% Extracting the 3D points of the reconstructed checkerboard corners
detected3DPoints = zeros(size(imagePoints, 1), 3);
validIndices = true(size(imagePoints, 1), 1);

for i = 1:size(imagePoints, 1)
x = round(imagePoints(i, 1));
y = round(imagePoints(i, 2));
if x > 0 && y > 0 && x <= size(points3D, 2) && y <= size(points3D, 1)
detected3DPoints(i, 🙂 = points3D(y, x, :);
if any(isnan(detected3DPoints(i, :)) | isinf(detected3DPoints(i, :)))
validIndices(i) = false;
end
else
validIndices(i) = false;
end
end

% Excluding invalid points
% detected3DPoints = detected3DPoints(validIndices, :); %Reconstructed grid
% worldPoints = worldPoints(validIndices, :); %Ground truth grid

% Omitting scale transformation
detected3DPoints_mm = detected3DPoints;

% Comparison of ground truth and reconstructed results
figure;
plot3(worldPoints(:,1), worldPoints(:,2), worldPoints(:,3), ‘go’);
hold on;
plot3(detected3DPoints_mm(:,1), detected3DPoints_mm(:,2), detected3DPoints_mm(:,3), ‘rx’);
% Displaying numbers on each plot
for i = 1:size(worldPoints, 1)
text(worldPoints(i, 1), worldPoints(i, 2), worldPoints(i, 3), num2str(i), ‘Color’, ‘green’);
text(detected3DPoints_mm(i, 1), detected3DPoints_mm(i, 2), detected3DPoints_mm(i, 3), num2str(i), ‘Color’, ‘red’);
end
legend(‘True Points’, ‘Reconstructed Points’);
xlabel(‘X (mm)’);
ylabel(‘Y (mm)’);
zlabel(‘Z (mm)’);
title(‘Comparison of True and Reconstructed Points’);
grid on;

% Calculating the error
errors_xy = sqrt(sum((worldPoints(:, 1:2) – detected3DPoints_mm(:, 1:2)).^2, 2));
meanError_xy = mean(errors_xy);
disp([‘Mean Error in XY plane: ‘, num2str(meanError_xy), ‘ millimeters’]);Currently, I am facing three issues related to 3D reconstruction. As shown in the first attached image, I describe the x-axis as positive to the right and the y-axis as positive upwards (since the checkerboard is planar, I will not consider the z-axis in this question). The green plots mean the ground truth, and the red plots mean reconstructed values in the first attached image.
When considering the detected checkerboard corner points in the same x×y layout as the premise, the ground truth is detected and plotted as 8×11, while the reconstructed values are detected and plotted as 10×7. Why is there a difference of one cell in the detected grid? Please refer to the attached checkerboard image for this situation.
As seen in the attached image, the long side of the ground truth checkerboard is aligned along the y-axis direction, while the long side of the reconstructed checkerboard appears to be aligned along the x-axis direction. Why does it seem like the long side’s position has rotated during reconstruction?
The first detected corner point of the reconstructed values should be located at the same origin as the first detected corner point of the ground truth, but it is located in a different position. What is the cause of this? Is this due to low accuracy in the camera parameters, or is it because the reconstructed values and the ground truth are represented in different coordinate systems? Could there be other reasons for this?
The specific causes for the second and third questions might be the same, but I appreciate your response. For reference, I am attaching the code as well.

% Loading stereo images
I1 = imread(‘/Users/uchidataisei/dev/uchida/kenkyu_data/2024-06-14/C3/GX010541_frames/frame0052.png’); % 左画像
I2 = imread(‘/Users/uchidataisei/dev/uchida/kenkyu_data/2024-06-14/C2/GX010611_frames/frame0052.png’); % 右画像

% Using pre-obtained calibration parameters
load(‘/Users/uchidataisei/dev/uchida/MATLAB_data/2024_06_14/calibrationSession_0614_C3_2_.mat’);

% Rectification
% [J1, J2, reprojectionMatrix] = rectifyStereoImages(I1, I2, stereoParams_0614_C3_2);
[J1, J2, reprojectionMatrix] = rectifyStereoImages(I1, I2, stereoParams_0614_C3_2_);

% Calculating the disparity map
disparityMap = disparitySGM(rgb2gray(J1), rgb2gray(J2));

% Reconstructing the 3D scene
points3D = reconstructScene(disparityMap, reprojectionMatrix);

% Setting the ground truth of the checkerboard, [mm]
squareSize = 114;

% Detecting the corners of the checkerboard
[imagePoints, boardSize] = detectCheckerboardPoints(J1);

% Calculating the ground truth grid of the checkerboard
[worldX, worldY] = meshgrid(0:squareSize:((boardSize(1)-1)*squareSize), 0:squareSize:((boardSize(2)-1)*squareSize));
worldPoints = [worldX(:), worldY(:), zeros(numel(worldX), 1)]; % Z=0

% Matching the number of detected corners with the number of ground truth points
% if size(worldPoints, 1) > size(imagePoints, 1)
% worldPoints = worldPoints(1:size(imagePoints, 1), :);
% elseif size(worldPoints, 1) < size(imagePoints, 1)
% error(‘The number of ground truth points is less than the number of detected corners. Please check the number of ground truth points.’);
% end

% Extracting the 3D points of the reconstructed checkerboard corners
detected3DPoints = zeros(size(imagePoints, 1), 3);
validIndices = true(size(imagePoints, 1), 1);

for i = 1:size(imagePoints, 1)
x = round(imagePoints(i, 1));
y = round(imagePoints(i, 2));
if x > 0 && y > 0 && x <= size(points3D, 2) && y <= size(points3D, 1)
detected3DPoints(i, 🙂 = points3D(y, x, :);
if any(isnan(detected3DPoints(i, :)) | isinf(detected3DPoints(i, :)))
validIndices(i) = false;
end
else
validIndices(i) = false;
end
end

% Excluding invalid points
% detected3DPoints = detected3DPoints(validIndices, :); %Reconstructed grid
% worldPoints = worldPoints(validIndices, :); %Ground truth grid

% Omitting scale transformation
detected3DPoints_mm = detected3DPoints;

% Comparison of ground truth and reconstructed results
figure;
plot3(worldPoints(:,1), worldPoints(:,2), worldPoints(:,3), ‘go’);
hold on;
plot3(detected3DPoints_mm(:,1), detected3DPoints_mm(:,2), detected3DPoints_mm(:,3), ‘rx’);
% Displaying numbers on each plot
for i = 1:size(worldPoints, 1)
text(worldPoints(i, 1), worldPoints(i, 2), worldPoints(i, 3), num2str(i), ‘Color’, ‘green’);
text(detected3DPoints_mm(i, 1), detected3DPoints_mm(i, 2), detected3DPoints_mm(i, 3), num2str(i), ‘Color’, ‘red’);
end
legend(‘True Points’, ‘Reconstructed Points’);
xlabel(‘X (mm)’);
ylabel(‘Y (mm)’);
zlabel(‘Z (mm)’);
title(‘Comparison of True and Reconstructed Points’);
grid on;

% Calculating the error
errors_xy = sqrt(sum((worldPoints(:, 1:2) – detected3DPoints_mm(:, 1:2)).^2, 2));
meanError_xy = mean(errors_xy);
disp([‘Mean Error in XY plane: ‘, num2str(meanError_xy), ‘ millimeters’]); Currently, I am facing three issues related to 3D reconstruction. As shown in the first attached image, I describe the x-axis as positive to the right and the y-axis as positive upwards (since the checkerboard is planar, I will not consider the z-axis in this question). The green plots mean the ground truth, and the red plots mean reconstructed values in the first attached image.
When considering the detected checkerboard corner points in the same x×y layout as the premise, the ground truth is detected and plotted as 8×11, while the reconstructed values are detected and plotted as 10×7. Why is there a difference of one cell in the detected grid? Please refer to the attached checkerboard image for this situation.
As seen in the attached image, the long side of the ground truth checkerboard is aligned along the y-axis direction, while the long side of the reconstructed checkerboard appears to be aligned along the x-axis direction. Why does it seem like the long side’s position has rotated during reconstruction?
The first detected corner point of the reconstructed values should be located at the same origin as the first detected corner point of the ground truth, but it is located in a different position. What is the cause of this? Is this due to low accuracy in the camera parameters, or is it because the reconstructed values and the ground truth are represented in different coordinate systems? Could there be other reasons for this?
The specific causes for the second and third questions might be the same, but I appreciate your response. For reference, I am attaching the code as well.

% Loading stereo images
I1 = imread(‘/Users/uchidataisei/dev/uchida/kenkyu_data/2024-06-14/C3/GX010541_frames/frame0052.png’); % 左画像
I2 = imread(‘/Users/uchidataisei/dev/uchida/kenkyu_data/2024-06-14/C2/GX010611_frames/frame0052.png’); % 右画像

% Using pre-obtained calibration parameters
load(‘/Users/uchidataisei/dev/uchida/MATLAB_data/2024_06_14/calibrationSession_0614_C3_2_.mat’);

% Rectification
% [J1, J2, reprojectionMatrix] = rectifyStereoImages(I1, I2, stereoParams_0614_C3_2);
[J1, J2, reprojectionMatrix] = rectifyStereoImages(I1, I2, stereoParams_0614_C3_2_);

% Calculating the disparity map
disparityMap = disparitySGM(rgb2gray(J1), rgb2gray(J2));

% Reconstructing the 3D scene
points3D = reconstructScene(disparityMap, reprojectionMatrix);

% Setting the ground truth of the checkerboard, [mm]
squareSize = 114;

% Detecting the corners of the checkerboard
[imagePoints, boardSize] = detectCheckerboardPoints(J1);

% Calculating the ground truth grid of the checkerboard
[worldX, worldY] = meshgrid(0:squareSize:((boardSize(1)-1)*squareSize), 0:squareSize:((boardSize(2)-1)*squareSize));
worldPoints = [worldX(:), worldY(:), zeros(numel(worldX), 1)]; % Z=0

% Matching the number of detected corners with the number of ground truth points
% if size(worldPoints, 1) > size(imagePoints, 1)
% worldPoints = worldPoints(1:size(imagePoints, 1), :);
% elseif size(worldPoints, 1) < size(imagePoints, 1)
% error(‘The number of ground truth points is less than the number of detected corners. Please check the number of ground truth points.’);
% end

% Extracting the 3D points of the reconstructed checkerboard corners
detected3DPoints = zeros(size(imagePoints, 1), 3);
validIndices = true(size(imagePoints, 1), 1);

for i = 1:size(imagePoints, 1)
x = round(imagePoints(i, 1));
y = round(imagePoints(i, 2));
if x > 0 && y > 0 && x <= size(points3D, 2) && y <= size(points3D, 1)
detected3DPoints(i, 🙂 = points3D(y, x, :);
if any(isnan(detected3DPoints(i, :)) | isinf(detected3DPoints(i, :)))
validIndices(i) = false;
end
else
validIndices(i) = false;
end
end

% Excluding invalid points
% detected3DPoints = detected3DPoints(validIndices, :); %Reconstructed grid
% worldPoints = worldPoints(validIndices, :); %Ground truth grid

% Omitting scale transformation
detected3DPoints_mm = detected3DPoints;

% Comparison of ground truth and reconstructed results
figure;
plot3(worldPoints(:,1), worldPoints(:,2), worldPoints(:,3), ‘go’);
hold on;
plot3(detected3DPoints_mm(:,1), detected3DPoints_mm(:,2), detected3DPoints_mm(:,3), ‘rx’);
% Displaying numbers on each plot
for i = 1:size(worldPoints, 1)
text(worldPoints(i, 1), worldPoints(i, 2), worldPoints(i, 3), num2str(i), ‘Color’, ‘green’);
text(detected3DPoints_mm(i, 1), detected3DPoints_mm(i, 2), detected3DPoints_mm(i, 3), num2str(i), ‘Color’, ‘red’);
end
legend(‘True Points’, ‘Reconstructed Points’);
xlabel(‘X (mm)’);
ylabel(‘Y (mm)’);
zlabel(‘Z (mm)’);
title(‘Comparison of True and Reconstructed Points’);
grid on;

% Calculating the error
errors_xy = sqrt(sum((worldPoints(:, 1:2) – detected3DPoints_mm(:, 1:2)).^2, 2));
meanError_xy = mean(errors_xy);
disp([‘Mean Error in XY plane: ‘, num2str(meanError_xy), ‘ millimeters’]); #reconstructscene, stereoparameters, detectcheckerboardpoints, 3d reconstruction MATLAB Answers — New Questions

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I am working with a slightly modified version of ode113. Until I was using the same code I copied and pasted and modified a few versions ago. Now in 2024A I got an error message, so I copied the code of ode113 again into a new file and modified again. However, a new problem has risen- it seems that 2023 is dependant on private functions, and I am now getting "unrecognized function or variable" errors. A workaround that I found is copying all the relevant private folders into a new folder. Is that the best way to solve the issue?

many thanks

NathanI am working with a slightly modified version of ode113. Until I was using the same code I copied and pasted and modified a few versions ago. Now in 2024A I got an error message, so I copied the code of ode113 again into a new file and modified again. However, a new problem has risen- it seems that 2023 is dependant on private functions, and I am now getting "unrecognized function or variable" errors. A workaround that I found is copying all the relevant private folders into a new folder. Is that the best way to solve the issue?

many thanks

Nathan I am working with a slightly modified version of ode113. Until I was using the same code I copied and pasted and modified a few versions ago. Now in 2024A I got an error message, so I copied the code of ode113 again into a new file and modified again. However, a new problem has risen- it seems that 2023 is dependant on private functions, and I am now getting "unrecognized function or variable" errors. A workaround that I found is copying all the relevant private folders into a new folder. Is that the best way to solve the issue?

many thanks

Nathan private, private functions, modified MATLAB Answers — New Questions

​

I get two different results from running this water-tank system example for reinforcement learning made by Mathworks:
https://uk.mathworks.com/help/reinforcement-learning/ug/create-simulink-environment-and-train-agent.html
This example has fixed the random number generator seed rng(0), so I expected the result to be the same on all computer. However, I ended up with two different agents on two computers:
Computer A finished training the agent after 86 episodes (just like the published example) and gave me an identical agent to the example.
Computer B needed 182 episodes to train the agent and gave me a different agent.
Both computers run MATLAB R2023b 64-bit on MS Windows 10. The code is unchanged from the example (except for changing doTraining = false to doTraining = true).
Computer A has an 8-core i7 processor. Computer B has a 6-core i7 processor.
I’m writing a tutorial for a univeristy-level course, so reproducibility is necessary so that students can follow the example. Any tip on how to facilitate this is also much appreciated.I get two different results from running this water-tank system example for reinforcement learning made by Mathworks:
https://uk.mathworks.com/help/reinforcement-learning/ug/create-simulink-environment-and-train-agent.html
This example has fixed the random number generator seed rng(0), so I expected the result to be the same on all computer. However, I ended up with two different agents on two computers:
Computer A finished training the agent after 86 episodes (just like the published example) and gave me an identical agent to the example.
Computer B needed 182 episodes to train the agent and gave me a different agent.
Both computers run MATLAB R2023b 64-bit on MS Windows 10. The code is unchanged from the example (except for changing doTraining = false to doTraining = true).
Computer A has an 8-core i7 processor. Computer B has a 6-core i7 processor.
I’m writing a tutorial for a univeristy-level course, so reproducibility is necessary so that students can follow the example. Any tip on how to facilitate this is also much appreciated. I get two different results from running this water-tank system example for reinforcement learning made by Mathworks:
https://uk.mathworks.com/help/reinforcement-learning/ug/create-simulink-environment-and-train-agent.html
This example has fixed the random number generator seed rng(0), so I expected the result to be the same on all computer. However, I ended up with two different agents on two computers:
Computer A finished training the agent after 86 episodes (just like the published example) and gave me an identical agent to the example.
Computer B needed 182 episodes to train the agent and gave me a different agent.
Both computers run MATLAB R2023b 64-bit on MS Windows 10. The code is unchanged from the example (except for changing doTraining = false to doTraining = true).
Computer A has an 8-core i7 processor. Computer B has a 6-core i7 processor.
I’m writing a tutorial for a univeristy-level course, so reproducibility is necessary so that students can follow the example. Any tip on how to facilitate this is also much appreciated. reinforcement learning, agent, training, random number generator MATLAB Answers — New Questions

​

am getting error in scanning in zigzag
the error is as follows

Undefined function or variable ‘toZigzag’.

Error in rlc_haar (line 21)

ImageArray=toZigzag(QuantizedImage);

plese help me

%% Matlab code for Image Compression Using Run Length Encoding

clc;
clear;
close all;
%% Set Quantization Parameter
quantizedvalue=10;
%% Read Input Image
InputImage=imread(‘cameraman.tif’);
[row col p]=size(InputImage);

%% Wavelet Decomposition
[LL LH HL HH]=dwt2(InputImage,’haar’);
WaveletDecomposeImage=[LL,LH;HL,HH];
imshow(WaveletDecomposeImage,[]);

%uniform quantization
QuantizedImage= WaveletDecomposeImage/quantizedvalue;
QuantizedImage= round(QuantizedImage);

% Convert the Two dimensional Image to a one dimensional Array using ZigZag Scanning
ImageArray=toZigzag(QuantizedImage);

%% Run Length Encoding
j=1;
a=length(ImageArray);
count=0;
for n=1:a
b=ImageArray(n);
if n==a
count=count+1;
c(j)=count;
s(j)=ImageArray(n);
elseif ImageArray(n)==ImageArray(n+1)
count=count+1;
elseif ImageArray(n)==b
count=count+1;
c(j)=count;
s(j)=ImageArray(n);
j=j+1;
count=0;
end
end

%% Calculation Bit Cost

InputBitcost=row*col*8;
InputBitcost=(InputBitcost);
c1=length(c);
s1=length(s);
OutputBitcost= (c1*8)+(s1*8);
OutputBitcost=(OutputBitcost);

%% Run Length Decoding g=length(s);
j=1;
l=1;
for i=1:g
v(l)=s(j);
if c(j)~=0
w=l+c(j)-1;
for p=l:w
v(l)=s(j);
l=l+1;
end
end
j=j+1;
end

ReconstructedImageArray=v;

%% Inverse ZigZag
ReconstructedImage=invZigzag(ReconstructedImageArray)

%% Inverse Quantization
ReconstructedImage=ReconstructedImage*quantizedvalue;am getting error in scanning in zigzag
the error is as follows

Undefined function or variable ‘toZigzag’.

Error in rlc_haar (line 21)

ImageArray=toZigzag(QuantizedImage);

plese help me

%% Matlab code for Image Compression Using Run Length Encoding

clc;
clear;
close all;
%% Set Quantization Parameter
quantizedvalue=10;
%% Read Input Image
InputImage=imread(‘cameraman.tif’);
[row col p]=size(InputImage);

%% Wavelet Decomposition
[LL LH HL HH]=dwt2(InputImage,’haar’);
WaveletDecomposeImage=[LL,LH;HL,HH];
imshow(WaveletDecomposeImage,[]);

%uniform quantization
QuantizedImage= WaveletDecomposeImage/quantizedvalue;
QuantizedImage= round(QuantizedImage);

% Convert the Two dimensional Image to a one dimensional Array using ZigZag Scanning
ImageArray=toZigzag(QuantizedImage);

%% Run Length Encoding
j=1;
a=length(ImageArray);
count=0;
for n=1:a
b=ImageArray(n);
if n==a
count=count+1;
c(j)=count;
s(j)=ImageArray(n);
elseif ImageArray(n)==ImageArray(n+1)
count=count+1;
elseif ImageArray(n)==b
count=count+1;
c(j)=count;
s(j)=ImageArray(n);
j=j+1;
count=0;
end
end

%% Calculation Bit Cost

InputBitcost=row*col*8;
InputBitcost=(InputBitcost);
c1=length(c);
s1=length(s);
OutputBitcost= (c1*8)+(s1*8);
OutputBitcost=(OutputBitcost);

%% Run Length Decoding g=length(s);
j=1;
l=1;
for i=1:g
v(l)=s(j);
if c(j)~=0
w=l+c(j)-1;
for p=l:w
v(l)=s(j);
l=l+1;
end
end
j=j+1;
end

ReconstructedImageArray=v;

%% Inverse ZigZag
ReconstructedImage=invZigzag(ReconstructedImageArray)

%% Inverse Quantization
ReconstructedImage=ReconstructedImage*quantizedvalue; am getting error in scanning in zigzag
the error is as follows

Undefined function or variable ‘toZigzag’.

Error in rlc_haar (line 21)

ImageArray=toZigzag(QuantizedImage);

plese help me

%% Matlab code for Image Compression Using Run Length Encoding

clc;
clear;
close all;
%% Set Quantization Parameter
quantizedvalue=10;
%% Read Input Image
InputImage=imread(‘cameraman.tif’);
[row col p]=size(InputImage);

%% Wavelet Decomposition
[LL LH HL HH]=dwt2(InputImage,’haar’);
WaveletDecomposeImage=[LL,LH;HL,HH];
imshow(WaveletDecomposeImage,[]);

%uniform quantization
QuantizedImage= WaveletDecomposeImage/quantizedvalue;
QuantizedImage= round(QuantizedImage);

% Convert the Two dimensional Image to a one dimensional Array using ZigZag Scanning
ImageArray=toZigzag(QuantizedImage);

%% Run Length Encoding
j=1;
a=length(ImageArray);
count=0;
for n=1:a
b=ImageArray(n);
if n==a
count=count+1;
c(j)=count;
s(j)=ImageArray(n);
elseif ImageArray(n)==ImageArray(n+1)
count=count+1;
elseif ImageArray(n)==b
count=count+1;
c(j)=count;
s(j)=ImageArray(n);
j=j+1;
count=0;
end
end

%% Calculation Bit Cost

InputBitcost=row*col*8;
InputBitcost=(InputBitcost);
c1=length(c);
s1=length(s);
OutputBitcost= (c1*8)+(s1*8);
OutputBitcost=(OutputBitcost);

%% Run Length Decoding g=length(s);
j=1;
l=1;
for i=1:g
v(l)=s(j);
if c(j)~=0
w=l+c(j)-1;
for p=l:w
v(l)=s(j);
l=l+1;
end
end
j=j+1;
end

ReconstructedImageArray=v;

%% Inverse ZigZag
ReconstructedImage=invZigzag(ReconstructedImageArray)

%% Inverse Quantization
ReconstructedImage=ReconstructedImage*quantizedvalue; . MATLAB Answers — New Questions

​

I am analyzing EMG data in windows. In each window, I apply z-score normalization to identify and remove outliers. To address the gaps created by removing these outliers, I attempt to fill the empty spaces with the median of the surrounding values. Additionally, I have experimented with MATLAB built-in functions such as ‘movmedian’ for this purpose.
here is my function:
function data_clean = remove_outliers_and_fill(data)
% Calculate z-scores for each column
z_scores = zscore(data);
% Define outlier threshold
threshold =3;
% Identify outliers
outliers = abs(z_scores) > threshold;
% Copy data to preserve original shape
data_clean = data;
% Loop through each column
[num_rows, num_cols] = size(data);
for col = 1:num_cols
for row = 1:num_rows
if outliers(row, col)
range_start = max(1, row-10);
range_end = min(num_rows, row+10);
neighbors = data(range_start:range_end, col);
% Exclude the outlier from median calculation
filtered_neighbors = neighbors(neighbors ~= data(row, col));
median_value = median(filtered_neighbors);
data_clean(row, col) = median_value;
end
end
end
end
here is the plot where it creates gaps after applying the above function.I am analyzing EMG data in windows. In each window, I apply z-score normalization to identify and remove outliers. To address the gaps created by removing these outliers, I attempt to fill the empty spaces with the median of the surrounding values. Additionally, I have experimented with MATLAB built-in functions such as ‘movmedian’ for this purpose.
here is my function:
function data_clean = remove_outliers_and_fill(data)
% Calculate z-scores for each column
z_scores = zscore(data);
% Define outlier threshold
threshold =3;
% Identify outliers
outliers = abs(z_scores) > threshold;
% Copy data to preserve original shape
data_clean = data;
% Loop through each column
[num_rows, num_cols] = size(data);
for col = 1:num_cols
for row = 1:num_rows
if outliers(row, col)
range_start = max(1, row-10);
range_end = min(num_rows, row+10);
neighbors = data(range_start:range_end, col);
% Exclude the outlier from median calculation
filtered_neighbors = neighbors(neighbors ~= data(row, col));
median_value = median(filtered_neighbors);
data_clean(row, col) = median_value;
end
end
end
end
here is the plot where it creates gaps after applying the above function. I am analyzing EMG data in windows. In each window, I apply z-score normalization to identify and remove outliers. To address the gaps created by removing these outliers, I attempt to fill the empty spaces with the median of the surrounding values. Additionally, I have experimented with MATLAB built-in functions such as ‘movmedian’ for this purpose.
here is my function:
function data_clean = remove_outliers_and_fill(data)
% Calculate z-scores for each column
z_scores = zscore(data);
% Define outlier threshold
threshold =3;
% Identify outliers
outliers = abs(z_scores) > threshold;
% Copy data to preserve original shape
data_clean = data;
% Loop through each column
[num_rows, num_cols] = size(data);
for col = 1:num_cols
for row = 1:num_rows
if outliers(row, col)
range_start = max(1, row-10);
range_end = min(num_rows, row+10);
neighbors = data(range_start:range_end, col);
% Exclude the outlier from median calculation
filtered_neighbors = neighbors(neighbors ~= data(row, col));
median_value = median(filtered_neighbors);
data_clean(row, col) = median_value;
end
end
end
end
here is the plot where it creates gaps after applying the above function. outliers, matlab function, emg signal MATLAB Answers — New Questions

​

I am using simscape battery toolbox for battery calendar aging simulation, with the battery table-based block, I enable the calendar aging function, I think which means my cell capacity now will decrease with the time

but I cannot find any port to output or somewhere to allow me to extract the battery capacity data. So I want to know, if I run a 100 weeks calendar aging simulation, and I want to obtain the capacity degradation curve (like the figure below, here I choose a cycle aging as an example, the axle x is the cycle life or time, the axle y is the remaining capacity)

how could I extract the battery capacity information from the Battery (table-based) block, if someone can help me, I will be very grateful, thanks.
(By the way, I have reviewed the help document regarding Battery (table-based) and Simscape battery, I also check the video "simscape battery essentials part 1 to 7" on Youtube, but cannot find the solution.)I am using simscape battery toolbox for battery calendar aging simulation, with the battery table-based block, I enable the calendar aging function, I think which means my cell capacity now will decrease with the time

but I cannot find any port to output or somewhere to allow me to extract the battery capacity data. So I want to know, if I run a 100 weeks calendar aging simulation, and I want to obtain the capacity degradation curve (like the figure below, here I choose a cycle aging as an example, the axle x is the cycle life or time, the axle y is the remaining capacity)

how could I extract the battery capacity information from the Battery (table-based) block, if someone can help me, I will be very grateful, thanks.
(By the way, I have reviewed the help document regarding Battery (table-based) and Simscape battery, I also check the video "simscape battery essentials part 1 to 7" on Youtube, but cannot find the solution.) I am using simscape battery toolbox for battery calendar aging simulation, with the battery table-based block, I enable the calendar aging function, I think which means my cell capacity now will decrease with the time

but I cannot find any port to output or somewhere to allow me to extract the battery capacity data. So I want to know, if I run a 100 weeks calendar aging simulation, and I want to obtain the capacity degradation curve (like the figure below, here I choose a cycle aging as an example, the axle x is the cycle life or time, the axle y is the remaining capacity)

how could I extract the battery capacity information from the Battery (table-based) block, if someone can help me, I will be very grateful, thanks.
(By the way, I have reviewed the help document regarding Battery (table-based) and Simscape battery, I also check the video "simscape battery essentials part 1 to 7" on Youtube, but cannot find the solution.) calendar aging, simscape battery, battery table-based block MATLAB Answers — New Questions

​

First i convert the image to an indexed image, and use only 5 colors to show what is my problem.

clear
img = imread(‘peppers.png’);
[x,map] = rgb2ind(img,5);

figure;
imhist(x,map)

<</matlabcentral/answers/uploaded_files/44260/imhist.PNG>>

the colorbar doesn’t match with the histogarm bars, this is my colormap:

map =

0.2784 0.1373 0.2353
0.7608 0.1686 0.1373
0.8902 0.7255 0.6353
0.4275 0.3765 0.2235
0.8471 0.5569 0.1020

the first one isn’t white, It seems that the colorbar is shifted by one.First i convert the image to an indexed image, and use only 5 colors to show what is my problem.

clear
img = imread(‘peppers.png’);
[x,map] = rgb2ind(img,5);

figure;
imhist(x,map)

<</matlabcentral/answers/uploaded_files/44260/imhist.PNG>>

the colorbar doesn’t match with the histogarm bars, this is my colormap:

map =

0.2784 0.1373 0.2353
0.7608 0.1686 0.1373
0.8902 0.7255 0.6353
0.4275 0.3765 0.2235
0.8471 0.5569 0.1020

the first one isn’t white, It seems that the colorbar is shifted by one. First i convert the image to an indexed image, and use only 5 colors to show what is my problem.

clear
img = imread(‘peppers.png’);
[x,map] = rgb2ind(img,5);

figure;
imhist(x,map)

<</matlabcentral/answers/uploaded_files/44260/imhist.PNG>>

the colorbar doesn’t match with the histogarm bars, this is my colormap:

map =

0.2784 0.1373 0.2353
0.7608 0.1686 0.1373
0.8902 0.7255 0.6353
0.4275 0.3765 0.2235
0.8471 0.5569 0.1020

the first one isn’t white, It seems that the colorbar is shifted by one. imhist, colorbar, indexed image MATLAB Answers — New Questions

​

I’ve been struggling to find a way to visualize the correlation between zscore (zscore.csv) and position (xcord.csv) from electrophysiological recordings. I only one dimension of position, but because the recordings were done over time I end up with 17940×2 per animal.
I tried to average z-scores into a new set of bins (new_bins.csv) that spams all the positions of all the animals, but don’t know if that would make a good visualization. Also tried to plot all the values as average of x values and average of z scores, but because there’s a clear preferance of side by the animal, I end up with too many datapoints on one end.

Is there a way to average the values of zscores respective to the values for x that fall into the new bins? Or is there a better way to visualize this altogether? The best example I found so far was panel D of this figure, but I don’t think it made it to their github.I’ve been struggling to find a way to visualize the correlation between zscore (zscore.csv) and position (xcord.csv) from electrophysiological recordings. I only one dimension of position, but because the recordings were done over time I end up with 17940×2 per animal.
I tried to average z-scores into a new set of bins (new_bins.csv) that spams all the positions of all the animals, but don’t know if that would make a good visualization. Also tried to plot all the values as average of x values and average of z scores, but because there’s a clear preferance of side by the animal, I end up with too many datapoints on one end.

Is there a way to average the values of zscores respective to the values for x that fall into the new bins? Or is there a better way to visualize this altogether? The best example I found so far was panel D of this figure, but I don’t think it made it to their github. I’ve been struggling to find a way to visualize the correlation between zscore (zscore.csv) and position (xcord.csv) from electrophysiological recordings. I only one dimension of position, but because the recordings were done over time I end up with 17940×2 per animal.
I tried to average z-scores into a new set of bins (new_bins.csv) that spams all the positions of all the animals, but don’t know if that would make a good visualization. Also tried to plot all the values as average of x values and average of z scores, but because there’s a clear preferance of side by the animal, I end up with too many datapoints on one end.

Is there a way to average the values of zscores respective to the values for x that fall into the new bins? Or is there a better way to visualize this altogether? The best example I found so far was panel D of this figure, but I don’t think it made it to their github. visualization, heatmaps, zscore MATLAB Answers — New Questions

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