In the attached app, there are 2 sliders for "set range low" and "set range high" which are used to change the scale of hte colorbar. The goal of doing this artifcats in the image.

Anything outside of the range of the colorbar is highlighted with the imdilate function in the mask which is then displayed as a replacement to the orignal image

When we click the buttons for "set range low" and "set range high", it should change the values of the image array so that anything outisde of the high and low limits is set to the actual high or low limit itself.
This seems to work, the problem is that the mask does not seem to reset. So after you click the buttons to "set the range low" in the image, if you go back to the slider and go outside of hte orignal range, there are still values that show up.

Im not sure what is going on, it seems to work ok in the matlab but not the application designer. Been stuck on this for an embarassing amount of time and could use a hand. thank you.

maxvalue = app.Sliderhigh.Value;
minvalue = app.Sliderlow.Value;
app.highEditField.Value = num2str(maxvalue);
app.lowEditField.Value= num2str(minvalue);
c = colorbar(app.UIAxes);
app.UIAxes.CLim = [minvalue maxvalue];

maska= app.a >maxvalue | app.a <minvalue ;
maskc = imdilate(maska,strel(‘disk’,25,0));
imagesc(maxvalue*maskc,’Parent’,app.UIAxes)In the attached app, there are 2 sliders for "set range low" and "set range high" which are used to change the scale of hte colorbar. The goal of doing this artifcats in the image.

Anything outside of the range of the colorbar is highlighted with the imdilate function in the mask which is then displayed as a replacement to the orignal image

When we click the buttons for "set range low" and "set range high", it should change the values of the image array so that anything outisde of the high and low limits is set to the actual high or low limit itself.
This seems to work, the problem is that the mask does not seem to reset. So after you click the buttons to "set the range low" in the image, if you go back to the slider and go outside of hte orignal range, there are still values that show up.

Im not sure what is going on, it seems to work ok in the matlab but not the application designer. Been stuck on this for an embarassing amount of time and could use a hand. thank you.

maxvalue = app.Sliderhigh.Value;
minvalue = app.Sliderlow.Value;
app.highEditField.Value = num2str(maxvalue);
app.lowEditField.Value= num2str(minvalue);
c = colorbar(app.UIAxes);
app.UIAxes.CLim = [minvalue maxvalue];

maska= app.a >maxvalue | app.a <minvalue ;
maskc = imdilate(maska,strel(‘disk’,25,0));
imagesc(maxvalue*maskc,’Parent’,app.UIAxes) In the attached app, there are 2 sliders for "set range low" and "set range high" which are used to change the scale of hte colorbar. The goal of doing this artifcats in the image.

Anything outside of the range of the colorbar is highlighted with the imdilate function in the mask which is then displayed as a replacement to the orignal image

When we click the buttons for "set range low" and "set range high", it should change the values of the image array so that anything outisde of the high and low limits is set to the actual high or low limit itself.
This seems to work, the problem is that the mask does not seem to reset. So after you click the buttons to "set the range low" in the image, if you go back to the slider and go outside of hte orignal range, there are still values that show up.

Im not sure what is going on, it seems to work ok in the matlab but not the application designer. Been stuck on this for an embarassing amount of time and could use a hand. thank you.

maxvalue = app.Sliderhigh.Value;
minvalue = app.Sliderlow.Value;
app.highEditField.Value = num2str(maxvalue);
app.lowEditField.Value= num2str(minvalue);
c = colorbar(app.UIAxes);
app.UIAxes.CLim = [minvalue maxvalue];

maska= app.a >maxvalue | app.a <minvalue ;
maskc = imdilate(maska,strel(‘disk’,25,0));
imagesc(maxvalue*maskc,’Parent’,app.UIAxes) application designer, mask MATLAB Answers — New Questions

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im using amd processor. while running the script from the editor it takes more and shows illegal instruction has been detected and matlab exist.im using amd processor. while running the script from the editor it takes more and shows illegal instruction has been detected and matlab exist. im using amd processor. while running the script from the editor it takes more and shows illegal instruction has been detected and matlab exist. script, instruction, environment variable MATLAB Answers — New Questions

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Dear,
how can I lock/unlock cells in Excell file?
For example: file = ‘Fruits.xlsx’, Sheet = ‘Apple’, Range = ‘B4:B10’.
How is possible to lock the cells in range ‘B4:B10’ in my file and my sheet? The file and the sheet are unprotected.
Thank you very much? Jurij RiciDear,
how can I lock/unlock cells in Excell file?
For example: file = ‘Fruits.xlsx’, Sheet = ‘Apple’, Range = ‘B4:B10’.
How is possible to lock the cells in range ‘B4:B10’ in my file and my sheet? The file and the sheet are unprotected.
Thank you very much? Jurij Rici Dear,
how can I lock/unlock cells in Excell file?
For example: file = ‘Fruits.xlsx’, Sheet = ‘Apple’, Range = ‘B4:B10’.
How is possible to lock the cells in range ‘B4:B10’ in my file and my sheet? The file and the sheet are unprotected.
Thank you very much? Jurij Rici excel, locked cells MATLAB Answers — New Questions

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In the mentioned Official Simulink Model, I do not know whether the model is tested using Speedgoat through rapid control prototyping or not. Can anyone please let me know.In the mentioned Official Simulink Model, I do not know whether the model is tested using Speedgoat through rapid control prototyping or not. Can anyone please let me know. In the mentioned Official Simulink Model, I do not know whether the model is tested using Speedgoat through rapid control prototyping or not. Can anyone please let me know. speedgoat, transferred MATLAB Answers — New Questions

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I am trying to train the rotatry inverted pendulum enviroment using a PPO agent. It’s working… but It’s reaching a limit and not learnign past this limit. I am not too sure why. Newbie to RL here so go easy on me :). I think it’s something to do with the yellow line, Q0. Also it could be reaching a local optima, but I don’t think this is the problem. I think the problem is with Q0 not getting past 100 and the agent not being able to extract more useful info. Hopefully, someone whith a little more experinace has something to say!
mdl = "rlQubeServoModel";
open_system(mdl)

theta_limit = 5*pi/8;
dtheta_limit = 30;
volt_limit = 12;
Ts = 0.005;

rng(22)

obsInfo = rlNumericSpec([7 1]);
actInfo = rlNumericSpec([1 1],UpperLimit=1,LowerLimit=-1);

agentBlk = mdl + "/RL Agent";
simEnv = rlSimulinkEnv(mdl,agentBlk,obsInfo,actInfo);

numObs = prod(obsInfo.Dimension);
criticLayerSizes = [400 300];
actorLayerSizes = [400 300];

% critic:
criticNetwork = [
featureInputLayer(numObs)
fullyConnectedLayer(criticLayerSizes(1), …
Weights=sqrt(2/numObs)*…
(rand(criticLayerSizes(1),numObs)-0.5), …
Bias=1e-3*ones(criticLayerSizes(1),1))
reluLayer
fullyConnectedLayer(criticLayerSizes(2), …
Weights=sqrt(2/criticLayerSizes(1))*…
(rand(criticLayerSizes(2),criticLayerSizes(1))-0.5), …
Bias=1e-3*ones(criticLayerSizes(2),1))
reluLayer
fullyConnectedLayer(1, …
Weights=sqrt(2/criticLayerSizes(2))* …
(rand(1,criticLayerSizes(2))-0.5), …
Bias=1e-3)
];

criticNetwork = dlnetwork(criticNetwork);
summary(criticNetwork)

critic = rlValueFunction(criticNetwork,obsInfo);

% actor:
% Input path layers
inPath = [
featureInputLayer( …
prod(obsInfo.Dimension), …
Name="netOin")
fullyConnectedLayer( …
prod(actInfo.Dimension), …
Name="infc")
];

% Path layers for mean value
meanPath = [
tanhLayer(Name="tanhMean");
fullyConnectedLayer(prod(actInfo.Dimension));
scalingLayer(Name="scale", …
Scale=actInfo.UpperLimit)
];

% Path layers for standard deviations
% Using softplus layer to make them non negative
sdevPath = [
tanhLayer(Name="tanhStdv");
fullyConnectedLayer(prod(actInfo.Dimension));
softplusLayer(Name="splus")
];

net = dlnetwork();
net = addLayers(net,inPath);
net = addLayers(net,meanPath);
net = addLayers(net,sdevPath);
net = connectLayers(net,"infc","tanhMean/in");
net = connectLayers(net,"infc","tanhStdv/in");
plot(net)
net = initialize(net);
summary(net)
actor = rlContinuousGaussianActor(net, obsInfo, actInfo, …
ActionMeanOutputNames="scale",…
ActionStandardDeviationOutputNames="splus",…
ObservationInputNames="netOin");

actorOpts = rlOptimizerOptions(LearnRate=1e-4);
criticOpts = rlOptimizerOptions(LearnRate=1e-4);
agentOpts = rlPPOAgentOptions(…
ExperienceHorizon=600,…
ClipFactor=0.02,…
EntropyLossWeight=0.01,…
ActorOptimizerOptions=actorOpts,…
CriticOptimizerOptions=criticOpts,…
NumEpoch=3,…
AdvantageEstimateMethod="gae",…
GAEFactor=0.95,…
SampleTime=0.1,…
DiscountFactor=0.997);
agent = rlPPOAgent(actor,critic,agentOpts);

trainOpts = rlTrainingOptions(…
MaxEpisodes=20000,…
MaxStepsPerEpisode=600,…
Plots="training-progress",…
StopTrainingCriteria="AverageReward",…
StopTrainingValue=430,…
ScoreAveragingWindowLength=100);

trainingStats = train(agent, simEnv, trainOpts);

thanks in advanced!I am trying to train the rotatry inverted pendulum enviroment using a PPO agent. It’s working… but It’s reaching a limit and not learnign past this limit. I am not too sure why. Newbie to RL here so go easy on me :). I think it’s something to do with the yellow line, Q0. Also it could be reaching a local optima, but I don’t think this is the problem. I think the problem is with Q0 not getting past 100 and the agent not being able to extract more useful info. Hopefully, someone whith a little more experinace has something to say!
mdl = "rlQubeServoModel";
open_system(mdl)

theta_limit = 5*pi/8;
dtheta_limit = 30;
volt_limit = 12;
Ts = 0.005;

rng(22)

obsInfo = rlNumericSpec([7 1]);
actInfo = rlNumericSpec([1 1],UpperLimit=1,LowerLimit=-1);

agentBlk = mdl + "/RL Agent";
simEnv = rlSimulinkEnv(mdl,agentBlk,obsInfo,actInfo);

numObs = prod(obsInfo.Dimension);
criticLayerSizes = [400 300];
actorLayerSizes = [400 300];

% critic:
criticNetwork = [
featureInputLayer(numObs)
fullyConnectedLayer(criticLayerSizes(1), …
Weights=sqrt(2/numObs)*…
(rand(criticLayerSizes(1),numObs)-0.5), …
Bias=1e-3*ones(criticLayerSizes(1),1))
reluLayer
fullyConnectedLayer(criticLayerSizes(2), …
Weights=sqrt(2/criticLayerSizes(1))*…
(rand(criticLayerSizes(2),criticLayerSizes(1))-0.5), …
Bias=1e-3*ones(criticLayerSizes(2),1))
reluLayer
fullyConnectedLayer(1, …
Weights=sqrt(2/criticLayerSizes(2))* …
(rand(1,criticLayerSizes(2))-0.5), …
Bias=1e-3)
];

criticNetwork = dlnetwork(criticNetwork);
summary(criticNetwork)

critic = rlValueFunction(criticNetwork,obsInfo);

% actor:
% Input path layers
inPath = [
featureInputLayer( …
prod(obsInfo.Dimension), …
Name="netOin")
fullyConnectedLayer( …
prod(actInfo.Dimension), …
Name="infc")
];

% Path layers for mean value
meanPath = [
tanhLayer(Name="tanhMean");
fullyConnectedLayer(prod(actInfo.Dimension));
scalingLayer(Name="scale", …
Scale=actInfo.UpperLimit)
];

% Path layers for standard deviations
% Using softplus layer to make them non negative
sdevPath = [
tanhLayer(Name="tanhStdv");
fullyConnectedLayer(prod(actInfo.Dimension));
softplusLayer(Name="splus")
];

net = dlnetwork();
net = addLayers(net,inPath);
net = addLayers(net,meanPath);
net = addLayers(net,sdevPath);
net = connectLayers(net,"infc","tanhMean/in");
net = connectLayers(net,"infc","tanhStdv/in");
plot(net)
net = initialize(net);
summary(net)
actor = rlContinuousGaussianActor(net, obsInfo, actInfo, …
ActionMeanOutputNames="scale",…
ActionStandardDeviationOutputNames="splus",…
ObservationInputNames="netOin");

actorOpts = rlOptimizerOptions(LearnRate=1e-4);
criticOpts = rlOptimizerOptions(LearnRate=1e-4);
agentOpts = rlPPOAgentOptions(…
ExperienceHorizon=600,…
ClipFactor=0.02,…
EntropyLossWeight=0.01,…
ActorOptimizerOptions=actorOpts,…
CriticOptimizerOptions=criticOpts,…
NumEpoch=3,…
AdvantageEstimateMethod="gae",…
GAEFactor=0.95,…
SampleTime=0.1,…
DiscountFactor=0.997);
agent = rlPPOAgent(actor,critic,agentOpts);

trainOpts = rlTrainingOptions(…
MaxEpisodes=20000,…
MaxStepsPerEpisode=600,…
Plots="training-progress",…
StopTrainingCriteria="AverageReward",…
StopTrainingValue=430,…
ScoreAveragingWindowLength=100);

trainingStats = train(agent, simEnv, trainOpts);

thanks in advanced! I am trying to train the rotatry inverted pendulum enviroment using a PPO agent. It’s working… but It’s reaching a limit and not learnign past this limit. I am not too sure why. Newbie to RL here so go easy on me :). I think it’s something to do with the yellow line, Q0. Also it could be reaching a local optima, but I don’t think this is the problem. I think the problem is with Q0 not getting past 100 and the agent not being able to extract more useful info. Hopefully, someone whith a little more experinace has something to say!
mdl = "rlQubeServoModel";
open_system(mdl)

theta_limit = 5*pi/8;
dtheta_limit = 30;
volt_limit = 12;
Ts = 0.005;

rng(22)

obsInfo = rlNumericSpec([7 1]);
actInfo = rlNumericSpec([1 1],UpperLimit=1,LowerLimit=-1);

agentBlk = mdl + "/RL Agent";
simEnv = rlSimulinkEnv(mdl,agentBlk,obsInfo,actInfo);

numObs = prod(obsInfo.Dimension);
criticLayerSizes = [400 300];
actorLayerSizes = [400 300];

% critic:
criticNetwork = [
featureInputLayer(numObs)
fullyConnectedLayer(criticLayerSizes(1), …
Weights=sqrt(2/numObs)*…
(rand(criticLayerSizes(1),numObs)-0.5), …
Bias=1e-3*ones(criticLayerSizes(1),1))
reluLayer
fullyConnectedLayer(criticLayerSizes(2), …
Weights=sqrt(2/criticLayerSizes(1))*…
(rand(criticLayerSizes(2),criticLayerSizes(1))-0.5), …
Bias=1e-3*ones(criticLayerSizes(2),1))
reluLayer
fullyConnectedLayer(1, …
Weights=sqrt(2/criticLayerSizes(2))* …
(rand(1,criticLayerSizes(2))-0.5), …
Bias=1e-3)
];

criticNetwork = dlnetwork(criticNetwork);
summary(criticNetwork)

critic = rlValueFunction(criticNetwork,obsInfo);

% actor:
% Input path layers
inPath = [
featureInputLayer( …
prod(obsInfo.Dimension), …
Name="netOin")
fullyConnectedLayer( …
prod(actInfo.Dimension), …
Name="infc")
];

% Path layers for mean value
meanPath = [
tanhLayer(Name="tanhMean");
fullyConnectedLayer(prod(actInfo.Dimension));
scalingLayer(Name="scale", …
Scale=actInfo.UpperLimit)
];

% Path layers for standard deviations
% Using softplus layer to make them non negative
sdevPath = [
tanhLayer(Name="tanhStdv");
fullyConnectedLayer(prod(actInfo.Dimension));
softplusLayer(Name="splus")
];

net = dlnetwork();
net = addLayers(net,inPath);
net = addLayers(net,meanPath);
net = addLayers(net,sdevPath);
net = connectLayers(net,"infc","tanhMean/in");
net = connectLayers(net,"infc","tanhStdv/in");
plot(net)
net = initialize(net);
summary(net)
actor = rlContinuousGaussianActor(net, obsInfo, actInfo, …
ActionMeanOutputNames="scale",…
ActionStandardDeviationOutputNames="splus",…
ObservationInputNames="netOin");

actorOpts = rlOptimizerOptions(LearnRate=1e-4);
criticOpts = rlOptimizerOptions(LearnRate=1e-4);
agentOpts = rlPPOAgentOptions(…
ExperienceHorizon=600,…
ClipFactor=0.02,…
EntropyLossWeight=0.01,…
ActorOptimizerOptions=actorOpts,…
CriticOptimizerOptions=criticOpts,…
NumEpoch=3,…
AdvantageEstimateMethod="gae",…
GAEFactor=0.95,…
SampleTime=0.1,…
DiscountFactor=0.997);
agent = rlPPOAgent(actor,critic,agentOpts);

trainOpts = rlTrainingOptions(…
MaxEpisodes=20000,…
MaxStepsPerEpisode=600,…
Plots="training-progress",…
StopTrainingCriteria="AverageReward",…
StopTrainingValue=430,…
ScoreAveragingWindowLength=100);

trainingStats = train(agent, simEnv, trainOpts);

thanks in advanced! ppo, rl, machine learning, ml, help, code, deep learning MATLAB Answers — New Questions

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Hi. I have this file ‘DATA’. The first 3 columns are the nodes in space (X,Y,Z in [m]). The 4th column is not of interest. The fifth column is the velocity in [m/s].
Is it possible to visualize the motion (green direction) of a sphere (e.g., the red one in the figure) moving following the velocity values in the fifth column of DATA?
load("DATA.mat");

figure
plot3(DATA(:,1),DATA(:,2),DATA(:,3),’-k’,’LineWidth’,0.1);
hold on
plot3(DATA(1,1),DATA(1,2),DATA(1,3),’r.’,’MarkerSize’,30);
hold off
axis equal
xlabel(‘x’)
ylabel(‘y’)
zlabel(‘z’)
grid offHi. I have this file ‘DATA’. The first 3 columns are the nodes in space (X,Y,Z in [m]). The 4th column is not of interest. The fifth column is the velocity in [m/s].
Is it possible to visualize the motion (green direction) of a sphere (e.g., the red one in the figure) moving following the velocity values in the fifth column of DATA?
load("DATA.mat");

figure
plot3(DATA(:,1),DATA(:,2),DATA(:,3),’-k’,’LineWidth’,0.1);
hold on
plot3(DATA(1,1),DATA(1,2),DATA(1,3),’r.’,’MarkerSize’,30);
hold off
axis equal
xlabel(‘x’)
ylabel(‘y’)
zlabel(‘z’)
grid off Hi. I have this file ‘DATA’. The first 3 columns are the nodes in space (X,Y,Z in [m]). The 4th column is not of interest. The fifth column is the velocity in [m/s].
Is it possible to visualize the motion (green direction) of a sphere (e.g., the red one in the figure) moving following the velocity values in the fifth column of DATA?
load("DATA.mat");

figure
plot3(DATA(:,1),DATA(:,2),DATA(:,3),’-k’,’LineWidth’,0.1);
hold on
plot3(DATA(1,1),DATA(1,2),DATA(1,3),’r.’,’MarkerSize’,30);
hold off
axis equal
xlabel(‘x’)
ylabel(‘y’)
zlabel(‘z’)
grid off motion, trajectory MATLAB Answers — New Questions

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I want to supres the warning: "Warning: The specified amount of data was not returned within the Timeout period for ‘readline’.
‘tcpclient’ unable to read any data. For more information on possible reasons, see tcpclient Read Warnings.". I am using tcpipclient and the readline function.I want to supres the warning: "Warning: The specified amount of data was not returned within the Timeout period for ‘readline’.
‘tcpclient’ unable to read any data. For more information on possible reasons, see tcpclient Read Warnings.". I am using tcpipclient and the readline function. I want to supres the warning: "Warning: The specified amount of data was not returned within the Timeout period for ‘readline’.
‘tcpclient’ unable to read any data. For more information on possible reasons, see tcpclient Read Warnings.". I am using tcpipclient and the readline function. tcpipclient, warning MATLAB Answers — New Questions

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Hello everybody,
I’m trying to implement a genetic algorithm that works with custom data types in MATLAB. Each chromosome of the population is made of two parts: a string and a vector of integers.
The string part is the binary representation of a number and I need to perform admissibility checks over it after crossover and mutation operations in order to avoid that the number represented could exceed a certain value.
I’ve followed this example for working with custom data types but I still have a question: how do I pass the max value as parameter to crossover and mutation functions? And, more generally, is there a way to pass any extra parameter to these functions?
For now I’m using global variables, but this feels a very bad idea and I’d like to find out which is the right way to do what I need.
Thanks in advance!Hello everybody,
I’m trying to implement a genetic algorithm that works with custom data types in MATLAB. Each chromosome of the population is made of two parts: a string and a vector of integers.
The string part is the binary representation of a number and I need to perform admissibility checks over it after crossover and mutation operations in order to avoid that the number represented could exceed a certain value.
I’ve followed this example for working with custom data types but I still have a question: how do I pass the max value as parameter to crossover and mutation functions? And, more generally, is there a way to pass any extra parameter to these functions?
For now I’m using global variables, but this feels a very bad idea and I’d like to find out which is the right way to do what I need.
Thanks in advance! Hello everybody,
I’m trying to implement a genetic algorithm that works with custom data types in MATLAB. Each chromosome of the population is made of two parts: a string and a vector of integers.
The string part is the binary representation of a number and I need to perform admissibility checks over it after crossover and mutation operations in order to avoid that the number represented could exceed a certain value.
I’ve followed this example for working with custom data types but I still have a question: how do I pass the max value as parameter to crossover and mutation functions? And, more generally, is there a way to pass any extra parameter to these functions?
For now I’m using global variables, but this feels a very bad idea and I’d like to find out which is the right way to do what I need.
Thanks in advance! genetic algorithm MATLAB Answers — New Questions

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Hi, I am trying to dispaly 3 images using the below command. My A, B, C are in the format of 656 x 875 x 3 unit 8. I think the error is because of unit 8 format. I tried imfuse() as well, although it works for two images, but not for three images.
A = imread(‘1.bmp’);
B = imread(‘2.bmp’);
C = imread(‘3.bmp’);
overlay_im = cat(3, A, B, C);
imshow(overlay_im);
However, the above code throws below error
Error in imshow (line 253)
images.internal.imageDisplayParseInputs({‘Parent’,’Border’,’Reduce’},preparsed_varargin{:});

Error in image_comb (line 276)
imshow(overlay_im);Hi, I am trying to dispaly 3 images using the below command. My A, B, C are in the format of 656 x 875 x 3 unit 8. I think the error is because of unit 8 format. I tried imfuse() as well, although it works for two images, but not for three images.
A = imread(‘1.bmp’);
B = imread(‘2.bmp’);
C = imread(‘3.bmp’);
overlay_im = cat(3, A, B, C);
imshow(overlay_im);
However, the above code throws below error
Error in imshow (line 253)
images.internal.imageDisplayParseInputs({‘Parent’,’Border’,’Reduce’},preparsed_varargin{:});

Error in image_comb (line 276)
imshow(overlay_im); Hi, I am trying to dispaly 3 images using the below command. My A, B, C are in the format of 656 x 875 x 3 unit 8. I think the error is because of unit 8 format. I tried imfuse() as well, although it works for two images, but not for three images.
A = imread(‘1.bmp’);
B = imread(‘2.bmp’);
C = imread(‘3.bmp’);
overlay_im = cat(3, A, B, C);
imshow(overlay_im);
However, the above code throws below error
Error in imshow (line 253)
images.internal.imageDisplayParseInputs({‘Parent’,’Border’,’Reduce’},preparsed_varargin{:});

Error in image_comb (line 276)
imshow(overlay_im); imfuse, display multiple images, combine images, imshowpair(), cat MATLAB Answers — New Questions

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I would like to load a shapefile (.shp) into Roadrunner, and while I can verify that the map appears correctly in QGIS, when I load the .shp and .prj files into Roadrunner, some parts are corrupted and not loaded properly. What could be the cause of this issue?
Thank you
(RoadRunner 2024a)I would like to load a shapefile (.shp) into Roadrunner, and while I can verify that the map appears correctly in QGIS, when I load the .shp and .prj files into Roadrunner, some parts are corrupted and not loaded properly. What could be the cause of this issue?
Thank you
(RoadRunner 2024a) I would like to load a shapefile (.shp) into Roadrunner, and while I can verify that the map appears correctly in QGIS, when I load the .shp and .prj files into Roadrunner, some parts are corrupted and not loaded properly. What could be the cause of this issue?
Thank you
(RoadRunner 2024a) #roadrunner, #shp MATLAB Answers — New Questions

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