The output of "diary" echoes what appears in the Command window, except for certain hyperlinks invoked by Matlab. Is there a way to echo literally what appears on the screen instead of code?
For example,
str = sprintf(‘%s%s’, …
‘<a href="matlab:magic(4)">’, …
‘Generate magic square</a>’);
disp(str)

MATLAB displays this in the Command Window:

However, the diary file, when viewed in a text editor, shows this text instead:
<a href="matlab:magic(4)">Generate magic square</a>

Thanks in advance…The output of "diary" echoes what appears in the Command window, except for certain hyperlinks invoked by Matlab. Is there a way to echo literally what appears on the screen instead of code?
For example,
str = sprintf(‘%s%s’, …
‘<a href="matlab:magic(4)">’, …
‘Generate magic square</a>’);
disp(str)

MATLAB displays this in the Command Window:

However, the diary file, when viewed in a text editor, shows this text instead:
<a href="matlab:magic(4)">Generate magic square</a>

Thanks in advance… The output of "diary" echoes what appears in the Command window, except for certain hyperlinks invoked by Matlab. Is there a way to echo literally what appears on the screen instead of code?
For example,
str = sprintf(‘%s%s’, …
‘<a href="matlab:magic(4)">’, …
‘Generate magic square</a>’);
disp(str)

MATLAB displays this in the Command Window:

However, the diary file, when viewed in a text editor, shows this text instead:
<a href="matlab:magic(4)">Generate magic square</a>

Thanks in advance… diary, literal text display, command window MATLAB Answers — New Questions

​

i installed matlab on my laptop(dell), but it did not open and with that, i could not be able to do anything at all, please help me on fixing it thanksi installed matlab on my laptop(dell), but it did not open and with that, i could not be able to do anything at all, please help me on fixing it thanks i installed matlab on my laptop(dell), but it did not open and with that, i could not be able to do anything at all, please help me on fixing it thanks un MATLAB Answers — New Questions

​

Hello everyone,
I am trying to reproduce the numerical results of a published tumour–immune ODE model that includes Dirac-type impulsive inputs. The system has four coupled variables [U,Y,Z,X][U, Y, Z, X][U,Y,Z,X]. Between injections, the dynamics are continuous, and at each injection time an instantaneous jump is applied to UUU:
Continuous part:
dU/dt = –kYU·U
dY/dt = kYU·U – kEY·Y
dZ/dt = kZY·Y – kEZ·Z
dX/dt = kX·X – kEX·X – η·Y·X·exp(–λ·Z)
At each dose time t=ti:
U(ti+)=U(ti−)+dosei×massi​
I have implemented this in MATLAB using two scripts:
A main script, where I loop over the dose times, call ode45 between impulses, and apply the jump in UUU. For example:
% Mouse 1 parameters and initial conditions
y_curr = [M1.U0; M1.Y0; M1.Z0; M1.X0];
t_curr = M1.t0;

for i = 1:length(tDose1)
ti = tDose1(i);
if ti > M1.tend, break; end

% integrate continuously until next dose
[t_seg, Y_seg] = ode45(@(t,y) fM1_fun(t,y,M1), [t_curr ti], y_curr, options);

% store results (code omitted here)
if isempty(t_all1)
t_all1 = t_seg;
Y_all1 = Y_seg;
else
t_all1 = [t_all1; t_seg(2:end,:)];
Y_all1 = [Y_all1; Y_seg(2:end,:)];
end

% state just before the impulse
y_curr = Y_seg(end,:)’;

% apply Dirac-type impulse to U
Mi = mass_fun1(ti); % interpolated mass at ti
ui = uDose1(i); % dose at ti (mg/kg)
y_curr(1) = y_curr(1) + ui * Mi; % jump in U

t_curr = ti;
end
2. ODE function files, one for each parameter set (Mouse 1 and Mouse 2). For example, for Mouse 1:
function dydt = fM1_fun(t,y,M1)

dU = -M1.kYU * y(1);
dY = M1.kYU * y(1) – M1.kEY * y(2);
dZ = M1.kZY * y(2) – M1.kEZ * y(3);
dX = M1.kX * y(4) – M1.kEX * y(4) …
– M1.etaEY * y(2) .* y(4) .* exp(-M1.lambda * y(3));

dydt = [dU; dY; dZ; dX];
end
The code runs without errors and the qualitative behaviour is reasonable, but the resulting trajectories do not match the published figures, even though the ODEs, parameters, dose times, and mass values were reproduced as closely as possible from the article.
Since all variables depend on U(t), any mismatch in handling the discontinuities propagates through the entire system.
I am unsure whether the mismatch is due to solver handling of the discontinuities, or to something subtle in the way I am implementing the impulses.
I would really appreciate if someone could look at this structure and tell me if I am handling the Dirac-type impulses and the splitting of the integration in a correct way for ode45, or if there is anything obviously wrong or missing in this implementation that could explain why my simulated curves do not match the published plots.
Thank you in advance for any help or suggestions.Hello everyone,
I am trying to reproduce the numerical results of a published tumour–immune ODE model that includes Dirac-type impulsive inputs. The system has four coupled variables [U,Y,Z,X][U, Y, Z, X][U,Y,Z,X]. Between injections, the dynamics are continuous, and at each injection time an instantaneous jump is applied to UUU:
Continuous part:
dU/dt = –kYU·U
dY/dt = kYU·U – kEY·Y
dZ/dt = kZY·Y – kEZ·Z
dX/dt = kX·X – kEX·X – η·Y·X·exp(–λ·Z)
At each dose time t=ti:
U(ti+)=U(ti−)+dosei×massi​
I have implemented this in MATLAB using two scripts:
A main script, where I loop over the dose times, call ode45 between impulses, and apply the jump in UUU. For example:
% Mouse 1 parameters and initial conditions
y_curr = [M1.U0; M1.Y0; M1.Z0; M1.X0];
t_curr = M1.t0;

for i = 1:length(tDose1)
ti = tDose1(i);
if ti > M1.tend, break; end

% integrate continuously until next dose
[t_seg, Y_seg] = ode45(@(t,y) fM1_fun(t,y,M1), [t_curr ti], y_curr, options);

% store results (code omitted here)
if isempty(t_all1)
t_all1 = t_seg;
Y_all1 = Y_seg;
else
t_all1 = [t_all1; t_seg(2:end,:)];
Y_all1 = [Y_all1; Y_seg(2:end,:)];
end

% state just before the impulse
y_curr = Y_seg(end,:)’;

% apply Dirac-type impulse to U
Mi = mass_fun1(ti); % interpolated mass at ti
ui = uDose1(i); % dose at ti (mg/kg)
y_curr(1) = y_curr(1) + ui * Mi; % jump in U

t_curr = ti;
end
2. ODE function files, one for each parameter set (Mouse 1 and Mouse 2). For example, for Mouse 1:
function dydt = fM1_fun(t,y,M1)

dU = -M1.kYU * y(1);
dY = M1.kYU * y(1) – M1.kEY * y(2);
dZ = M1.kZY * y(2) – M1.kEZ * y(3);
dX = M1.kX * y(4) – M1.kEX * y(4) …
– M1.etaEY * y(2) .* y(4) .* exp(-M1.lambda * y(3));

dydt = [dU; dY; dZ; dX];
end
The code runs without errors and the qualitative behaviour is reasonable, but the resulting trajectories do not match the published figures, even though the ODEs, parameters, dose times, and mass values were reproduced as closely as possible from the article.
Since all variables depend on U(t), any mismatch in handling the discontinuities propagates through the entire system.
I am unsure whether the mismatch is due to solver handling of the discontinuities, or to something subtle in the way I am implementing the impulses.
I would really appreciate if someone could look at this structure and tell me if I am handling the Dirac-type impulses and the splitting of the integration in a correct way for ode45, or if there is anything obviously wrong or missing in this implementation that could explain why my simulated curves do not match the published plots.
Thank you in advance for any help or suggestions. Hello everyone,
I am trying to reproduce the numerical results of a published tumour–immune ODE model that includes Dirac-type impulsive inputs. The system has four coupled variables [U,Y,Z,X][U, Y, Z, X][U,Y,Z,X]. Between injections, the dynamics are continuous, and at each injection time an instantaneous jump is applied to UUU:
Continuous part:
dU/dt = –kYU·U
dY/dt = kYU·U – kEY·Y
dZ/dt = kZY·Y – kEZ·Z
dX/dt = kX·X – kEX·X – η·Y·X·exp(–λ·Z)
At each dose time t=ti:
U(ti+)=U(ti−)+dosei×massi​
I have implemented this in MATLAB using two scripts:
A main script, where I loop over the dose times, call ode45 between impulses, and apply the jump in UUU. For example:
% Mouse 1 parameters and initial conditions
y_curr = [M1.U0; M1.Y0; M1.Z0; M1.X0];
t_curr = M1.t0;

for i = 1:length(tDose1)
ti = tDose1(i);
if ti > M1.tend, break; end

% integrate continuously until next dose
[t_seg, Y_seg] = ode45(@(t,y) fM1_fun(t,y,M1), [t_curr ti], y_curr, options);

% store results (code omitted here)
if isempty(t_all1)
t_all1 = t_seg;
Y_all1 = Y_seg;
else
t_all1 = [t_all1; t_seg(2:end,:)];
Y_all1 = [Y_all1; Y_seg(2:end,:)];
end

% state just before the impulse
y_curr = Y_seg(end,:)’;

% apply Dirac-type impulse to U
Mi = mass_fun1(ti); % interpolated mass at ti
ui = uDose1(i); % dose at ti (mg/kg)
y_curr(1) = y_curr(1) + ui * Mi; % jump in U

t_curr = ti;
end
2. ODE function files, one for each parameter set (Mouse 1 and Mouse 2). For example, for Mouse 1:
function dydt = fM1_fun(t,y,M1)

dU = -M1.kYU * y(1);
dY = M1.kYU * y(1) – M1.kEY * y(2);
dZ = M1.kZY * y(2) – M1.kEZ * y(3);
dX = M1.kX * y(4) – M1.kEX * y(4) …
– M1.etaEY * y(2) .* y(4) .* exp(-M1.lambda * y(3));

dydt = [dU; dY; dZ; dX];
end
The code runs without errors and the qualitative behaviour is reasonable, but the resulting trajectories do not match the published figures, even though the ODEs, parameters, dose times, and mass values were reproduced as closely as possible from the article.
Since all variables depend on U(t), any mismatch in handling the discontinuities propagates through the entire system.
I am unsure whether the mismatch is due to solver handling of the discontinuities, or to something subtle in the way I am implementing the impulses.
I would really appreciate if someone could look at this structure and tell me if I am handling the Dirac-type impulses and the splitting of the integration in a correct way for ode45, or if there is anything obviously wrong or missing in this implementation that could explain why my simulated curves do not match the published plots.
Thank you in advance for any help or suggestions. #bioinformatic #ode #rungekutta #dirac MATLAB Answers — New Questions

​

import cv2
import numpy as np
import os

output_folder = r"E:grains_output"
# Create the folder if it doesn’t exist
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Example: saving an image
img = cv2.imread(r"C:UsersDixuDownloadstest1.png") # your grain image
if img is None:
print("Error: Image not found! Check the path and filename.")
exit()
else:
print("Image loaded successfully!")
cv2.imwrite(os.path.join(output_folder, "segmented.png"), img)

# FUNCTION: Separate grains using COLOR segmentation (HSV
def separate_grains(image_path):
print("Loading:", image_path)
img = cv2.imread(image_path)
if img is None:
print("Error: image not found!")
return None, None
# Convert to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Yellow grains range
lower = np.array([15, 40, 60])
upper = np.array([40, 255, 255])
# Mask grains
mask = cv2.inRange(hsv, lower, upper)

# Morphology cleanup
kernel = np.ones((5, 5), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2)

# Convert mask to binary
ret, thresh = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)

# Noise removal
kernel = np.ones((3, 3), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
# Sure background area
sure_bg = cv2.dilate(opening, kernel, iterations=3)

# Sure foreground area
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist_transform, 0.6 * dist_transform.max(), 255, 0)
# Unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
# Marker labeling
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers + 1 # background = 1
markers[unknown == 255] = 0

# Apply watershed
markers = cv2.watershed(img, markers)
mask[markers == -1] = 0 # boundaries
return mask, img

def save_grains(mask, img):
# print("Saving grains…")

output_dir = output_folder # Use the folder defined at the top
os.makedirs(output_dir, exist_ok=True)

# Find contours
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

grain_id = 1
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if w < 10 or h < 10:
continue
grain = img[y:y + h, x:x + w]
filename = os.path.join(output_dir, f"grain_{grain_id}.png")
cv2.imwrite(filename, grain)
print("Saved:", filename)
grain_id += 1

print("nAll grains saved inside folder:", output_dir)
# Draw contours on a copy of the original image
result = img.copy()
cv2.drawContours(result, contours, -1, (0, 0, 255), 2)
# Save the segmented image
segmented_path = os.path.join(output_dir, "segmented_contours.png")
cv2.imwrite(segmented_path, result)
print("Segmented image with contours saved as:", segmented_path)

return result

# ———————————————————
# MAIN EXECUTION
# ———————————————————
if __name__ == "__main__":

# print("Current working directory:", os.getcwd())
mask, img = separate_grains(r"C:UsersDixuDownloadstest1.png")
if mask is not None:
result_img = save_grains(mask, img)

# # Show results
# cv2.namedWindow("Mask", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("Mask", 600, 500)
# cv2.imshow("Mask", mask)

cv2.namedWindow("Segmented Image", cv2.WINDOW_NORMAL)
cv2.resizeWindow("Segmented Image", 600, 500)
cv2.imshow("Segmented Image", result_img)
cv2.waitKey(0)
cv2.destroyAllWindows()import cv2
import numpy as np
import os

output_folder = r"E:grains_output"
# Create the folder if it doesn’t exist
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Example: saving an image
img = cv2.imread(r"C:UsersDixuDownloadstest1.png") # your grain image
if img is None:
print("Error: Image not found! Check the path and filename.")
exit()
else:
print("Image loaded successfully!")
cv2.imwrite(os.path.join(output_folder, "segmented.png"), img)

# FUNCTION: Separate grains using COLOR segmentation (HSV
def separate_grains(image_path):
print("Loading:", image_path)
img = cv2.imread(image_path)
if img is None:
print("Error: image not found!")
return None, None
# Convert to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Yellow grains range
lower = np.array([15, 40, 60])
upper = np.array([40, 255, 255])
# Mask grains
mask = cv2.inRange(hsv, lower, upper)

# Morphology cleanup
kernel = np.ones((5, 5), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2)

# Convert mask to binary
ret, thresh = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)

# Noise removal
kernel = np.ones((3, 3), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
# Sure background area
sure_bg = cv2.dilate(opening, kernel, iterations=3)

# Sure foreground area
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist_transform, 0.6 * dist_transform.max(), 255, 0)
# Unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
# Marker labeling
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers + 1 # background = 1
markers[unknown == 255] = 0

# Apply watershed
markers = cv2.watershed(img, markers)
mask[markers == -1] = 0 # boundaries
return mask, img

def save_grains(mask, img):
# print("Saving grains…")

output_dir = output_folder # Use the folder defined at the top
os.makedirs(output_dir, exist_ok=True)

# Find contours
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

grain_id = 1
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if w < 10 or h < 10:
continue
grain = img[y:y + h, x:x + w]
filename = os.path.join(output_dir, f"grain_{grain_id}.png")
cv2.imwrite(filename, grain)
print("Saved:", filename)
grain_id += 1

print("nAll grains saved inside folder:", output_dir)
# Draw contours on a copy of the original image
result = img.copy()
cv2.drawContours(result, contours, -1, (0, 0, 255), 2)
# Save the segmented image
segmented_path = os.path.join(output_dir, "segmented_contours.png")
cv2.imwrite(segmented_path, result)
print("Segmented image with contours saved as:", segmented_path)

return result

# ———————————————————
# MAIN EXECUTION
# ———————————————————
if __name__ == "__main__":

# print("Current working directory:", os.getcwd())
mask, img = separate_grains(r"C:UsersDixuDownloadstest1.png")
if mask is not None:
result_img = save_grains(mask, img)

# # Show results
# cv2.namedWindow("Mask", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("Mask", 600, 500)
# cv2.imshow("Mask", mask)

cv2.namedWindow("Segmented Image", cv2.WINDOW_NORMAL)
cv2.resizeWindow("Segmented Image", 600, 500)
cv2.imshow("Segmented Image", result_img)
cv2.waitKey(0)
cv2.destroyAllWindows() import cv2
import numpy as np
import os

output_folder = r"E:grains_output"
# Create the folder if it doesn’t exist
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# Example: saving an image
img = cv2.imread(r"C:UsersDixuDownloadstest1.png") # your grain image
if img is None:
print("Error: Image not found! Check the path and filename.")
exit()
else:
print("Image loaded successfully!")
cv2.imwrite(os.path.join(output_folder, "segmented.png"), img)

# FUNCTION: Separate grains using COLOR segmentation (HSV
def separate_grains(image_path):
print("Loading:", image_path)
img = cv2.imread(image_path)
if img is None:
print("Error: image not found!")
return None, None
# Convert to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Yellow grains range
lower = np.array([15, 40, 60])
upper = np.array([40, 255, 255])
# Mask grains
mask = cv2.inRange(hsv, lower, upper)

# Morphology cleanup
kernel = np.ones((5, 5), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2)

# Convert mask to binary
ret, thresh = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)

# Noise removal
kernel = np.ones((3, 3), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
# Sure background area
sure_bg = cv2.dilate(opening, kernel, iterations=3)

# Sure foreground area
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist_transform, 0.6 * dist_transform.max(), 255, 0)
# Unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
# Marker labeling
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers + 1 # background = 1
markers[unknown == 255] = 0

# Apply watershed
markers = cv2.watershed(img, markers)
mask[markers == -1] = 0 # boundaries
return mask, img

def save_grains(mask, img):
# print("Saving grains…")

output_dir = output_folder # Use the folder defined at the top
os.makedirs(output_dir, exist_ok=True)

# Find contours
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

grain_id = 1
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if w < 10 or h < 10:
continue
grain = img[y:y + h, x:x + w]
filename = os.path.join(output_dir, f"grain_{grain_id}.png")
cv2.imwrite(filename, grain)
print("Saved:", filename)
grain_id += 1

print("nAll grains saved inside folder:", output_dir)
# Draw contours on a copy of the original image
result = img.copy()
cv2.drawContours(result, contours, -1, (0, 0, 255), 2)
# Save the segmented image
segmented_path = os.path.join(output_dir, "segmented_contours.png")
cv2.imwrite(segmented_path, result)
print("Segmented image with contours saved as:", segmented_path)

return result

# ———————————————————
# MAIN EXECUTION
# ———————————————————
if __name__ == "__main__":

# print("Current working directory:", os.getcwd())
mask, img = separate_grains(r"C:UsersDixuDownloadstest1.png")
if mask is not None:
result_img = save_grains(mask, img)

# # Show results
# cv2.namedWindow("Mask", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("Mask", 600, 500)
# cv2.imshow("Mask", mask)

cv2.namedWindow("Segmented Image", cv2.WINDOW_NORMAL)
cv2.resizeWindow("Segmented Image", 600, 500)
cv2.imshow("Segmented Image", result_img)
cv2.waitKey(0)
cv2.destroyAllWindows() python, cv2, image processing, image segmentation MATLAB Answers — New Questions

​

Hey guys, when I make the custom text function for dataCursorMode it keeps on showing this when clicking on the marker on the plot
Here is the code for the plot
function WinChampgraph_team_selector
data2 = readtable("alltimeteams.xlsx",’VariableNamingRule’,’preserve’);

% Calculate the win percentage for each team
winPercent = (data2.Wins ./ (data2.Wins + data2.Losses));

hold on

graph = plot(winPercent, data2.Championships,"LineStyle","none","Color","r","Marker","*");

dcm = datacursormode;
dcm.Enable = ‘on’;
dcm.UpdateFcn = @displayteam;
getCursorInfo(dcm);

displayteam(data2);

xlabel(‘Win Percentage’);
ylabel(‘Championships’);
title(‘Team performance since the beginning of the NBA’);
grid on;
set(gcf,’Position’,[50,500,700,500])

end

Here is the code for the custom text function
function txt = displayteam(data3)
x = data3.Franchise;

y = (data3.Wins ./ (data3.Wins + data3.Losses));

myDatatipText = "(%s, %s, %s)";
txt = sprintf(myDatatipText, string(x), string(y),data3.Championships);
end
Then it shows this on the plot
Unable to update data tip using custom update function
Please lmk is anything else is neededHey guys, when I make the custom text function for dataCursorMode it keeps on showing this when clicking on the marker on the plot
Here is the code for the plot
function WinChampgraph_team_selector
data2 = readtable("alltimeteams.xlsx",’VariableNamingRule’,’preserve’);

% Calculate the win percentage for each team
winPercent = (data2.Wins ./ (data2.Wins + data2.Losses));

hold on

graph = plot(winPercent, data2.Championships,"LineStyle","none","Color","r","Marker","*");

dcm = datacursormode;
dcm.Enable = ‘on’;
dcm.UpdateFcn = @displayteam;
getCursorInfo(dcm);

displayteam(data2);

xlabel(‘Win Percentage’);
ylabel(‘Championships’);
title(‘Team performance since the beginning of the NBA’);
grid on;
set(gcf,’Position’,[50,500,700,500])

end

Here is the code for the custom text function
function txt = displayteam(data3)
x = data3.Franchise;

y = (data3.Wins ./ (data3.Wins + data3.Losses));

myDatatipText = "(%s, %s, %s)";
txt = sprintf(myDatatipText, string(x), string(y),data3.Championships);
end
Then it shows this on the plot
Unable to update data tip using custom update function
Please lmk is anything else is needed Hey guys, when I make the custom text function for dataCursorMode it keeps on showing this when clicking on the marker on the plot
Here is the code for the plot
function WinChampgraph_team_selector
data2 = readtable("alltimeteams.xlsx",’VariableNamingRule’,’preserve’);

% Calculate the win percentage for each team
winPercent = (data2.Wins ./ (data2.Wins + data2.Losses));

hold on

graph = plot(winPercent, data2.Championships,"LineStyle","none","Color","r","Marker","*");

dcm = datacursormode;
dcm.Enable = ‘on’;
dcm.UpdateFcn = @displayteam;
getCursorInfo(dcm);

displayteam(data2);

xlabel(‘Win Percentage’);
ylabel(‘Championships’);
title(‘Team performance since the beginning of the NBA’);
grid on;
set(gcf,’Position’,[50,500,700,500])

end

Here is the code for the custom text function
function txt = displayteam(data3)
x = data3.Franchise;

y = (data3.Wins ./ (data3.Wins + data3.Losses));

myDatatipText = "(%s, %s, %s)";
txt = sprintf(myDatatipText, string(x), string(y),data3.Championships);
end
Then it shows this on the plot
Unable to update data tip using custom update function
Please lmk is anything else is needed plot MATLAB Answers — New Questions

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Hello Community,
I generally work on 3D simulation tools associated with domains such as Powertrain NVH studies and I am new to Matlab-Simulink tools.
Now I am exploring the electrical/electronics domain (beginner) to get some initial data that will be required for further NVH studies.
I have made a 3-Phase Inverter with Pulse Width Modulation (PWM) circuit with help of tutorials online.
Currently I am facing this issue, where the output current extracted from the 3 Phase Inverter is not having the same magnitude(values) in
3 phases, How to correct the Matlab-simulink model regarding this issue?
Inputs given to the model,
DC Voltage source – 400 V
and Reference Sine wave blocks – Frequency 50Hz and Phase difference of 120 for 3 phases (Amplitude 1)
and carrier wave (Sawtooth generator) – Frequency 10,000 Hz (Amplitude 1)
The Load – 3-Phase Parallel RLC Branch – Only Resistor type is used with 1 Ohm resistance

Note: Regarding the grounding, two approaches I have tried – Grounding the load (RLC) and grounding the Inverter 6-switches loop (both have same results)
Thank you in advance for all the support and helpHello Community,
I generally work on 3D simulation tools associated with domains such as Powertrain NVH studies and I am new to Matlab-Simulink tools.
Now I am exploring the electrical/electronics domain (beginner) to get some initial data that will be required for further NVH studies.
I have made a 3-Phase Inverter with Pulse Width Modulation (PWM) circuit with help of tutorials online.
Currently I am facing this issue, where the output current extracted from the 3 Phase Inverter is not having the same magnitude(values) in
3 phases, How to correct the Matlab-simulink model regarding this issue?
Inputs given to the model,
DC Voltage source – 400 V
and Reference Sine wave blocks – Frequency 50Hz and Phase difference of 120 for 3 phases (Amplitude 1)
and carrier wave (Sawtooth generator) – Frequency 10,000 Hz (Amplitude 1)
The Load – 3-Phase Parallel RLC Branch – Only Resistor type is used with 1 Ohm resistance

Note: Regarding the grounding, two approaches I have tried – Grounding the load (RLC) and grounding the Inverter 6-switches loop (both have same results)
Thank you in advance for all the support and help Hello Community,
I generally work on 3D simulation tools associated with domains such as Powertrain NVH studies and I am new to Matlab-Simulink tools.
Now I am exploring the electrical/electronics domain (beginner) to get some initial data that will be required for further NVH studies.
I have made a 3-Phase Inverter with Pulse Width Modulation (PWM) circuit with help of tutorials online.
Currently I am facing this issue, where the output current extracted from the 3 Phase Inverter is not having the same magnitude(values) in
3 phases, How to correct the Matlab-simulink model regarding this issue?
Inputs given to the model,
DC Voltage source – 400 V
and Reference Sine wave blocks – Frequency 50Hz and Phase difference of 120 for 3 phases (Amplitude 1)
and carrier wave (Sawtooth generator) – Frequency 10,000 Hz (Amplitude 1)
The Load – 3-Phase Parallel RLC Branch – Only Resistor type is used with 1 Ohm resistance

Note: Regarding the grounding, two approaches I have tried – Grounding the load (RLC) and grounding the Inverter 6-switches loop (both have same results)
Thank you in advance for all the support and help three-phase inverters, pulse width modulation (pwm), simulink MATLAB Answers — New Questions

​