Author: PuTI
addon installer says i dont own the addon still after changing accounts
Hi, i was using personal account with trial and then i changed to my schools academic license on matlab but i still can’t download stuff from addon installer and i have the addons on my license. It seems addon installer is not updated on my account because it is still showing me that i have 27 days of trial left. It is true for my personal account but not for my academic account.
update: a new installation fixed this problem.Hi, i was using personal account with trial and then i changed to my schools academic license on matlab but i still can’t download stuff from addon installer and i have the addons on my license. It seems addon installer is not updated on my account because it is still showing me that i have 27 days of trial left. It is true for my personal account but not for my academic account.
update: a new installation fixed this problem. Hi, i was using personal account with trial and then i changed to my schools academic license on matlab but i still can’t download stuff from addon installer and i have the addons on my license. It seems addon installer is not updated on my account because it is still showing me that i have 27 days of trial left. It is true for my personal account but not for my academic account.
update: a new installation fixed this problem. addons, account, license MATLAB Answers — New Questions
How to get values of pre-filled holes in dotx template
Hi,
I would like to import a template.dotx that contains holes, some being already pre-filled beforehand.
For instance: Imagine that a holeID "ahole1" that contains in the dotx a predefined value x, written when creating the dotx.
In the script, I would like to read the value of "ahole1" prior deciding what to write in a second hole "ahole2".
Pratical example:
dotx is created with "ahole1" value = 10 and "ahole2" is empty.
dotx is imported into matlab
If the value of "ahole1"> 12, a second hole "ahole2" will have appended into it "5"
I am using matlab 2024b and the report generator to import the dotx, but i cannot find the means to get the content of a Hole unless from the script level i append a value to it.Hi,
I would like to import a template.dotx that contains holes, some being already pre-filled beforehand.
For instance: Imagine that a holeID "ahole1" that contains in the dotx a predefined value x, written when creating the dotx.
In the script, I would like to read the value of "ahole1" prior deciding what to write in a second hole "ahole2".
Pratical example:
dotx is created with "ahole1" value = 10 and "ahole2" is empty.
dotx is imported into matlab
If the value of "ahole1"> 12, a second hole "ahole2" will have appended into it "5"
I am using matlab 2024b and the report generator to import the dotx, but i cannot find the means to get the content of a Hole unless from the script level i append a value to it. Hi,
I would like to import a template.dotx that contains holes, some being already pre-filled beforehand.
For instance: Imagine that a holeID "ahole1" that contains in the dotx a predefined value x, written when creating the dotx.
In the script, I would like to read the value of "ahole1" prior deciding what to write in a second hole "ahole2".
Pratical example:
dotx is created with "ahole1" value = 10 and "ahole2" is empty.
dotx is imported into matlab
If the value of "ahole1"> 12, a second hole "ahole2" will have appended into it "5"
I am using matlab 2024b and the report generator to import the dotx, but i cannot find the means to get the content of a Hole unless from the script level i append a value to it. dotx, pre-filled holes, hole value, dotx reporter generator MATLAB Answers — New Questions
I need help for drawing a constraint in Matlab in 2D
Dear All,
I’m trying to draw a set of constraints but I got a wrong line for the constraint 2×1<=3 !!!! I need a vertical line !! I approciate any help from you !!!
Many Thanks,
Nadia
clear;clc;close;
%max 5×1+7×2;
%3×1+8×2<=12;
%x1+x2<=2;
%2×1<=3;
%x2<=4;
%x1,x2>=0;
%% plot the feasible region %Generate data
[x1,x2] = meshgrid(0:0.1:10);% i changed 0.1 to 0.01 to plot the fourth constraint
NB=5*x1+7*x2;
% Get True where condition applies, false where not.
cond1=3*x1+8*x2<=12; cond2=x1+x2<=2; cond3=2*x1<=3; cond4=x2<=2;
% Get boundaries of the condition
p1=(12-3*x1(1,:))/8;
p2=2-x1(1,:);
p3=3/2-x1(1,:);
p4=2-x2(1,:);
%Delete Areas whereCondition does not apply;
NB(~cond1)=NaN; NB(~cond2)=NaN; NB(~cond3)=NaN; NB(~cond4)=NaN;
%% Plot
[x2,h]=contourf(x1,x2,NB,0); % command contourf for filling the area between the contour levels
hold on
plot(x1(1,:),p1,’r’,’LineWidth’,2); text(x1(1,20),p1(20), ‘leftarrow Cond1’); %arbitrary location
plot(x1(1,:),p2,’k’,’LineWidth’,2); text(x1(1,15),p2(15), ‘leftarrow Cond2’); %arbitrary location
plot(x1(1,:),p3,’b’,’LineWidth’,2); text(x1(1,20),p3(20), ‘leftarrow Cond3’); %arbitrary location
plot(x1(1,:),p4,’b’,’LineWidth’,2); text(x1(1,20),p4(20), ‘leftarrow Cond3’); %arbitrary location
axis([0 5 0 5])
xlabel(‘x1’)
ylabel(‘x2’)Dear All,
I’m trying to draw a set of constraints but I got a wrong line for the constraint 2×1<=3 !!!! I need a vertical line !! I approciate any help from you !!!
Many Thanks,
Nadia
clear;clc;close;
%max 5×1+7×2;
%3×1+8×2<=12;
%x1+x2<=2;
%2×1<=3;
%x2<=4;
%x1,x2>=0;
%% plot the feasible region %Generate data
[x1,x2] = meshgrid(0:0.1:10);% i changed 0.1 to 0.01 to plot the fourth constraint
NB=5*x1+7*x2;
% Get True where condition applies, false where not.
cond1=3*x1+8*x2<=12; cond2=x1+x2<=2; cond3=2*x1<=3; cond4=x2<=2;
% Get boundaries of the condition
p1=(12-3*x1(1,:))/8;
p2=2-x1(1,:);
p3=3/2-x1(1,:);
p4=2-x2(1,:);
%Delete Areas whereCondition does not apply;
NB(~cond1)=NaN; NB(~cond2)=NaN; NB(~cond3)=NaN; NB(~cond4)=NaN;
%% Plot
[x2,h]=contourf(x1,x2,NB,0); % command contourf for filling the area between the contour levels
hold on
plot(x1(1,:),p1,’r’,’LineWidth’,2); text(x1(1,20),p1(20), ‘leftarrow Cond1’); %arbitrary location
plot(x1(1,:),p2,’k’,’LineWidth’,2); text(x1(1,15),p2(15), ‘leftarrow Cond2’); %arbitrary location
plot(x1(1,:),p3,’b’,’LineWidth’,2); text(x1(1,20),p3(20), ‘leftarrow Cond3’); %arbitrary location
plot(x1(1,:),p4,’b’,’LineWidth’,2); text(x1(1,20),p4(20), ‘leftarrow Cond3’); %arbitrary location
axis([0 5 0 5])
xlabel(‘x1’)
ylabel(‘x2’) Dear All,
I’m trying to draw a set of constraints but I got a wrong line for the constraint 2×1<=3 !!!! I need a vertical line !! I approciate any help from you !!!
Many Thanks,
Nadia
clear;clc;close;
%max 5×1+7×2;
%3×1+8×2<=12;
%x1+x2<=2;
%2×1<=3;
%x2<=4;
%x1,x2>=0;
%% plot the feasible region %Generate data
[x1,x2] = meshgrid(0:0.1:10);% i changed 0.1 to 0.01 to plot the fourth constraint
NB=5*x1+7*x2;
% Get True where condition applies, false where not.
cond1=3*x1+8*x2<=12; cond2=x1+x2<=2; cond3=2*x1<=3; cond4=x2<=2;
% Get boundaries of the condition
p1=(12-3*x1(1,:))/8;
p2=2-x1(1,:);
p3=3/2-x1(1,:);
p4=2-x2(1,:);
%Delete Areas whereCondition does not apply;
NB(~cond1)=NaN; NB(~cond2)=NaN; NB(~cond3)=NaN; NB(~cond4)=NaN;
%% Plot
[x2,h]=contourf(x1,x2,NB,0); % command contourf for filling the area between the contour levels
hold on
plot(x1(1,:),p1,’r’,’LineWidth’,2); text(x1(1,20),p1(20), ‘leftarrow Cond1’); %arbitrary location
plot(x1(1,:),p2,’k’,’LineWidth’,2); text(x1(1,15),p2(15), ‘leftarrow Cond2’); %arbitrary location
plot(x1(1,:),p3,’b’,’LineWidth’,2); text(x1(1,20),p3(20), ‘leftarrow Cond3’); %arbitrary location
plot(x1(1,:),p4,’b’,’LineWidth’,2); text(x1(1,20),p4(20), ‘leftarrow Cond3’); %arbitrary location
axis([0 5 0 5])
xlabel(‘x1’)
ylabel(‘x2’) drawing a constraint, vertical line, matlab MATLAB Answers — New Questions
How can I calculate the probability of false detection?
Hello everyone!
I need to justify for my dissertation the problems of false detection of a signal by a normal distribution (Gaussian) and build a graph where I should get a decreasing exponent in the interval for P from 10^-8 to 10^-1, and for alpha squared (alpha^2) from 10 to 100. The sigma dispersion = from 10 to 100. The threshold value of the signal n2 = 1.5, from which the normal value is integrated to infinity to calculate the probability of P.
I wrote the following code:
% Given parameters
sigma2_values = linspace (0.1, 0.01, 100); % dispersion values from 0.01 to 1000
n2 = 1.5; % threshold value
P_loznoe = zeros(length(sigma2_values), 1); % Initialize array for P_false
% Calculate P_false for each value of sigma^2 for fixed n2
for j = 1: length(sigma2_values)
sigma2 = sigma2_values(j); % use current value of sigma^2
alpha2 = 1/sigma2; % Calculate alpha^2
% Calculate integral of P(x) from n2 to infinity
integrand = @(x) (1 / (sqrt(2 * pi * sigma2))) .* exp(-((x.^2) / (2 * sigma2)));
P_loznoe(j) = integral(integrand, n2, Inf); % Calculate the integral
end
% Calculate alpha^2 for each sigma^2
alpha2_values = 1 ./sigma2_values; % alpha^2 = 1/sigma^2
% Plot P_false vs. alpha^2
figure;
semilogy(alpha2_values, P_loznoe, ‘r’, ‘LineWidth’, 2); % Logarithmic scale on the Y axis
title(‘False discovery rate vs. alpha^2’);
xlabel(‘alpha^2’);
ylabel(‘P_{false}’);
xlim([10 100]); % Set limits on the X axis
ylim([10^(-8) 10^(-1)]); % Set limits on the Y axis to expand the grid
grid on; % Grid on
But for some reason my graph is not in the specified interval and not in the form of a decreasing exponent, but in the form of a linear decrease.
How can this be fixed?
Thanks in advance!Hello everyone!
I need to justify for my dissertation the problems of false detection of a signal by a normal distribution (Gaussian) and build a graph where I should get a decreasing exponent in the interval for P from 10^-8 to 10^-1, and for alpha squared (alpha^2) from 10 to 100. The sigma dispersion = from 10 to 100. The threshold value of the signal n2 = 1.5, from which the normal value is integrated to infinity to calculate the probability of P.
I wrote the following code:
% Given parameters
sigma2_values = linspace (0.1, 0.01, 100); % dispersion values from 0.01 to 1000
n2 = 1.5; % threshold value
P_loznoe = zeros(length(sigma2_values), 1); % Initialize array for P_false
% Calculate P_false for each value of sigma^2 for fixed n2
for j = 1: length(sigma2_values)
sigma2 = sigma2_values(j); % use current value of sigma^2
alpha2 = 1/sigma2; % Calculate alpha^2
% Calculate integral of P(x) from n2 to infinity
integrand = @(x) (1 / (sqrt(2 * pi * sigma2))) .* exp(-((x.^2) / (2 * sigma2)));
P_loznoe(j) = integral(integrand, n2, Inf); % Calculate the integral
end
% Calculate alpha^2 for each sigma^2
alpha2_values = 1 ./sigma2_values; % alpha^2 = 1/sigma^2
% Plot P_false vs. alpha^2
figure;
semilogy(alpha2_values, P_loznoe, ‘r’, ‘LineWidth’, 2); % Logarithmic scale on the Y axis
title(‘False discovery rate vs. alpha^2’);
xlabel(‘alpha^2’);
ylabel(‘P_{false}’);
xlim([10 100]); % Set limits on the X axis
ylim([10^(-8) 10^(-1)]); % Set limits on the Y axis to expand the grid
grid on; % Grid on
But for some reason my graph is not in the specified interval and not in the form of a decreasing exponent, but in the form of a linear decrease.
How can this be fixed?
Thanks in advance! Hello everyone!
I need to justify for my dissertation the problems of false detection of a signal by a normal distribution (Gaussian) and build a graph where I should get a decreasing exponent in the interval for P from 10^-8 to 10^-1, and for alpha squared (alpha^2) from 10 to 100. The sigma dispersion = from 10 to 100. The threshold value of the signal n2 = 1.5, from which the normal value is integrated to infinity to calculate the probability of P.
I wrote the following code:
% Given parameters
sigma2_values = linspace (0.1, 0.01, 100); % dispersion values from 0.01 to 1000
n2 = 1.5; % threshold value
P_loznoe = zeros(length(sigma2_values), 1); % Initialize array for P_false
% Calculate P_false for each value of sigma^2 for fixed n2
for j = 1: length(sigma2_values)
sigma2 = sigma2_values(j); % use current value of sigma^2
alpha2 = 1/sigma2; % Calculate alpha^2
% Calculate integral of P(x) from n2 to infinity
integrand = @(x) (1 / (sqrt(2 * pi * sigma2))) .* exp(-((x.^2) / (2 * sigma2)));
P_loznoe(j) = integral(integrand, n2, Inf); % Calculate the integral
end
% Calculate alpha^2 for each sigma^2
alpha2_values = 1 ./sigma2_values; % alpha^2 = 1/sigma^2
% Plot P_false vs. alpha^2
figure;
semilogy(alpha2_values, P_loznoe, ‘r’, ‘LineWidth’, 2); % Logarithmic scale on the Y axis
title(‘False discovery rate vs. alpha^2’);
xlabel(‘alpha^2’);
ylabel(‘P_{false}’);
xlim([10 100]); % Set limits on the X axis
ylim([10^(-8) 10^(-1)]); % Set limits on the Y axis to expand the grid
grid on; % Grid on
But for some reason my graph is not in the specified interval and not in the form of a decreasing exponent, but in the form of a linear decrease.
How can this be fixed?
Thanks in advance! gauss MATLAB Answers — New Questions
liver tumor image segmentation
hi, I have a CT image 2D and the mask for it. I need to do a MATLAB code for U-NET that I can train for this images and also test images. Can you help me with some ideas? Thank you.hi, I have a CT image 2D and the mask for it. I need to do a MATLAB code for U-NET that I can train for this images and also test images. Can you help me with some ideas? Thank you. hi, I have a CT image 2D and the mask for it. I need to do a MATLAB code for U-NET that I can train for this images and also test images. Can you help me with some ideas? Thank you. image segmentation, deep learning MATLAB Answers — New Questions
unrecognized table variable name minLS in Bayesian optimization of TreeBagger
Hello,
I tried to do the hyperparameter tuning using bayesian optimization for the randomforest model I made using Treebagger. This is the code I used. I didnot use ensemble bagged trees in regression learner directly because eachtime i check the optimization the minimum mse output is like 498 learners and 1 minimum leaf size for 9 number of predictors.
264 inputTable=readtable(‘dataall_trainingregression.csv’);
265 predictorNames = {‘temp_diff’, ‘temp_median’, ‘NDVI’, ‘Clay’, ‘elevation’, ‘slope’, ‘TWI’, ‘sand’, ‘DOY’};
266 predictors = inputTable(:, predictorNames);
267 response = inputTable.daily_meanSM;
268 n=length(inputTable.daily_meanSM);
269 cvp = cvpartition(n,’KFold’,5);
271 maxMinLS = 20;
272 minLS = optimizableVariable(‘minLS’,[1,maxMinLS],’Type’,’integer’);
273 numPTS = optimizableVariable(‘numPTS’,[1,size(predictors,2)],’Type’,’integer’);
274 hyperparametersRF = [minLS; numPTS];
275 fun = @(params)crossval(‘mse’,predictors,response,’Predfun’,@myfunction,’Partition’,cvp);
277 results = bayesopt(fun,hyperparametersRF,…
‘AcquisitionFunctionName’,’expected-improvement-plus’,’Verbose’,0);
280 function yfit = myfunction(params,predictors,response,test)
281 Mdl1 = TreeBagger(30,predictors,response,…
282 ‘Method’,"regression",’Surrogate’,"on",…
283 ‘PredictorSelection’,"curvature",…
284 ‘OOBPredictorImportance’,"on",’MinLeafSize’,params.minLS,…
285 ‘NumPredictorsToSample’,params.numPTS);
286 yfit = predict(Mdl1,test);
288 end
for this code i receive an error message of :
Error using crossval>evalFun
The function ‘myfunction’ generated the following error:
Unrecognized table variable name ‘minLS’.
Error in crossval>getLossVal (line 529)
funResult = evalFun(funorStr,arg(1:end-1));
Error in crossval (line 428)
[funResult,outarg] = getLossVal(i, nData, cvp, data, predfun);
Error in model_hyperparameter_tuning>@(params)crossval(‘mse’,predictors,response,’Predfun’,@myfunction,’Partition’,cvp) (line 275)
fun = @(params)crossval(‘mse’,predictors,response,’Predfun’,@myfunction,’Partition’,cvp);
Error in BayesianOptimization/callObjNormally (line 13)
Objective = this.ObjectiveFcn(conditionalizeX(this, X));
Error in BayesianOptimization/callObjFcn (line 25)
= callObjNormally(this, X);
Error in BayesianOptimization/runSerial (line 24)
ObjectiveFcnObjectiveEvaluationTime, ObjectiveNargout] = callObjFcn(this, this.XNext);
Error in BayesianOptimization/run (line 9)
this = runSerial(this);
Error in BayesianOptimization (line 184)
this = run(this);
Error in bayesopt (line 323)
Results = BayesianOptimization(Options);
Error in model_hyperparameter_tuning (line 277)
results = bayesopt(fun,hyperparametersRF,…
can anyone please help me to solve this problem. ThanksHello,
I tried to do the hyperparameter tuning using bayesian optimization for the randomforest model I made using Treebagger. This is the code I used. I didnot use ensemble bagged trees in regression learner directly because eachtime i check the optimization the minimum mse output is like 498 learners and 1 minimum leaf size for 9 number of predictors.
264 inputTable=readtable(‘dataall_trainingregression.csv’);
265 predictorNames = {‘temp_diff’, ‘temp_median’, ‘NDVI’, ‘Clay’, ‘elevation’, ‘slope’, ‘TWI’, ‘sand’, ‘DOY’};
266 predictors = inputTable(:, predictorNames);
267 response = inputTable.daily_meanSM;
268 n=length(inputTable.daily_meanSM);
269 cvp = cvpartition(n,’KFold’,5);
271 maxMinLS = 20;
272 minLS = optimizableVariable(‘minLS’,[1,maxMinLS],’Type’,’integer’);
273 numPTS = optimizableVariable(‘numPTS’,[1,size(predictors,2)],’Type’,’integer’);
274 hyperparametersRF = [minLS; numPTS];
275 fun = @(params)crossval(‘mse’,predictors,response,’Predfun’,@myfunction,’Partition’,cvp);
277 results = bayesopt(fun,hyperparametersRF,…
‘AcquisitionFunctionName’,’expected-improvement-plus’,’Verbose’,0);
280 function yfit = myfunction(params,predictors,response,test)
281 Mdl1 = TreeBagger(30,predictors,response,…
282 ‘Method’,"regression",’Surrogate’,"on",…
283 ‘PredictorSelection’,"curvature",…
284 ‘OOBPredictorImportance’,"on",’MinLeafSize’,params.minLS,…
285 ‘NumPredictorsToSample’,params.numPTS);
286 yfit = predict(Mdl1,test);
288 end
for this code i receive an error message of :
Error using crossval>evalFun
The function ‘myfunction’ generated the following error:
Unrecognized table variable name ‘minLS’.
Error in crossval>getLossVal (line 529)
funResult = evalFun(funorStr,arg(1:end-1));
Error in crossval (line 428)
[funResult,outarg] = getLossVal(i, nData, cvp, data, predfun);
Error in model_hyperparameter_tuning>@(params)crossval(‘mse’,predictors,response,’Predfun’,@myfunction,’Partition’,cvp) (line 275)
fun = @(params)crossval(‘mse’,predictors,response,’Predfun’,@myfunction,’Partition’,cvp);
Error in BayesianOptimization/callObjNormally (line 13)
Objective = this.ObjectiveFcn(conditionalizeX(this, X));
Error in BayesianOptimization/callObjFcn (line 25)
= callObjNormally(this, X);
Error in BayesianOptimization/runSerial (line 24)
ObjectiveFcnObjectiveEvaluationTime, ObjectiveNargout] = callObjFcn(this, this.XNext);
Error in BayesianOptimization/run (line 9)
this = runSerial(this);
Error in BayesianOptimization (line 184)
this = run(this);
Error in bayesopt (line 323)
Results = BayesianOptimization(Options);
Error in model_hyperparameter_tuning (line 277)
results = bayesopt(fun,hyperparametersRF,…
can anyone please help me to solve this problem. Thanks Hello,
I tried to do the hyperparameter tuning using bayesian optimization for the randomforest model I made using Treebagger. This is the code I used. I didnot use ensemble bagged trees in regression learner directly because eachtime i check the optimization the minimum mse output is like 498 learners and 1 minimum leaf size for 9 number of predictors.
264 inputTable=readtable(‘dataall_trainingregression.csv’);
265 predictorNames = {‘temp_diff’, ‘temp_median’, ‘NDVI’, ‘Clay’, ‘elevation’, ‘slope’, ‘TWI’, ‘sand’, ‘DOY’};
266 predictors = inputTable(:, predictorNames);
267 response = inputTable.daily_meanSM;
268 n=length(inputTable.daily_meanSM);
269 cvp = cvpartition(n,’KFold’,5);
271 maxMinLS = 20;
272 minLS = optimizableVariable(‘minLS’,[1,maxMinLS],’Type’,’integer’);
273 numPTS = optimizableVariable(‘numPTS’,[1,size(predictors,2)],’Type’,’integer’);
274 hyperparametersRF = [minLS; numPTS];
275 fun = @(params)crossval(‘mse’,predictors,response,’Predfun’,@myfunction,’Partition’,cvp);
277 results = bayesopt(fun,hyperparametersRF,…
‘AcquisitionFunctionName’,’expected-improvement-plus’,’Verbose’,0);
280 function yfit = myfunction(params,predictors,response,test)
281 Mdl1 = TreeBagger(30,predictors,response,…
282 ‘Method’,"regression",’Surrogate’,"on",…
283 ‘PredictorSelection’,"curvature",…
284 ‘OOBPredictorImportance’,"on",’MinLeafSize’,params.minLS,…
285 ‘NumPredictorsToSample’,params.numPTS);
286 yfit = predict(Mdl1,test);
288 end
for this code i receive an error message of :
Error using crossval>evalFun
The function ‘myfunction’ generated the following error:
Unrecognized table variable name ‘minLS’.
Error in crossval>getLossVal (line 529)
funResult = evalFun(funorStr,arg(1:end-1));
Error in crossval (line 428)
[funResult,outarg] = getLossVal(i, nData, cvp, data, predfun);
Error in model_hyperparameter_tuning>@(params)crossval(‘mse’,predictors,response,’Predfun’,@myfunction,’Partition’,cvp) (line 275)
fun = @(params)crossval(‘mse’,predictors,response,’Predfun’,@myfunction,’Partition’,cvp);
Error in BayesianOptimization/callObjNormally (line 13)
Objective = this.ObjectiveFcn(conditionalizeX(this, X));
Error in BayesianOptimization/callObjFcn (line 25)
= callObjNormally(this, X);
Error in BayesianOptimization/runSerial (line 24)
ObjectiveFcnObjectiveEvaluationTime, ObjectiveNargout] = callObjFcn(this, this.XNext);
Error in BayesianOptimization/run (line 9)
this = runSerial(this);
Error in BayesianOptimization (line 184)
this = run(this);
Error in bayesopt (line 323)
Results = BayesianOptimization(Options);
Error in model_hyperparameter_tuning (line 277)
results = bayesopt(fun,hyperparametersRF,…
can anyone please help me to solve this problem. Thanks treebagger, bayesian optimization MATLAB Answers — New Questions
How to add newer CCSTUDIO version (12.8.0) in Matlab 2024a/b
Hello,
How do I add newer CCSTUDIO IDE version (v12.8.0) from TI in Matlab 2024a/b ?
Thanks for the guidance.
Regards,
JohnnyHello,
How do I add newer CCSTUDIO IDE version (v12.8.0) from TI in Matlab 2024a/b ?
Thanks for the guidance.
Regards,
Johnny Hello,
How do I add newer CCSTUDIO IDE version (v12.8.0) from TI in Matlab 2024a/b ?
Thanks for the guidance.
Regards,
Johnny c2000 microcontroller MATLAB Answers — New Questions
Identify an unrecognized function or variable
clear all; close all;
syms T U t w
a=0.3; M=1; h0=1; h1=1; k0=1; k1=1; p=0.5; d=0.5; Z=10;
%t=0:1:10; %w=0.75;
H=@(s) (p*Z-d-exp(-s))*k1;
K=@(r,g) p*Z*k1-(d+exp(-r))*k1-2*exp(a*r).*g;
for n=1:10
y=int(H(U),0,10);
yy= h0 + ((1-a)./M)*H(t).*h1 + (a./M)*y;
h1=yy;
end
for n=1:10
z=int(K(U,T),0,10);
zz= k0 + ((1-w)./M).*K(t,yy).*k1 + (w./M)*z;
k1=zz;
end
zzfcn = matlabFunction(zz)
t=0:0.1:10;
w = 0.1:0.1:0.75;
[T,W] = ndgrid(t,w);
figure
surf(T, W, zzfcn(T,W))
xlabel(‘t’)
ylabel(‘omega’)
zlabel(‘k(t)’)
set(gca, ‘ZScale’,’log’)
colormap(turbo)
colorbarclear all; close all;
syms T U t w
a=0.3; M=1; h0=1; h1=1; k0=1; k1=1; p=0.5; d=0.5; Z=10;
%t=0:1:10; %w=0.75;
H=@(s) (p*Z-d-exp(-s))*k1;
K=@(r,g) p*Z*k1-(d+exp(-r))*k1-2*exp(a*r).*g;
for n=1:10
y=int(H(U),0,10);
yy= h0 + ((1-a)./M)*H(t).*h1 + (a./M)*y;
h1=yy;
end
for n=1:10
z=int(K(U,T),0,10);
zz= k0 + ((1-w)./M).*K(t,yy).*k1 + (w./M)*z;
k1=zz;
end
zzfcn = matlabFunction(zz)
t=0:0.1:10;
w = 0.1:0.1:0.75;
[T,W] = ndgrid(t,w);
figure
surf(T, W, zzfcn(T,W))
xlabel(‘t’)
ylabel(‘omega’)
zlabel(‘k(t)’)
set(gca, ‘ZScale’,’log’)
colormap(turbo)
colorbar clear all; close all;
syms T U t w
a=0.3; M=1; h0=1; h1=1; k0=1; k1=1; p=0.5; d=0.5; Z=10;
%t=0:1:10; %w=0.75;
H=@(s) (p*Z-d-exp(-s))*k1;
K=@(r,g) p*Z*k1-(d+exp(-r))*k1-2*exp(a*r).*g;
for n=1:10
y=int(H(U),0,10);
yy= h0 + ((1-a)./M)*H(t).*h1 + (a./M)*y;
h1=yy;
end
for n=1:10
z=int(K(U,T),0,10);
zz= k0 + ((1-w)./M).*K(t,yy).*k1 + (w./M)*z;
k1=zz;
end
zzfcn = matlabFunction(zz)
t=0:0.1:10;
w = 0.1:0.1:0.75;
[T,W] = ndgrid(t,w);
figure
surf(T, W, zzfcn(T,W))
xlabel(‘t’)
ylabel(‘omega’)
zlabel(‘k(t)’)
set(gca, ‘ZScale’,’log’)
colormap(turbo)
colorbar function MATLAB Answers — New Questions
Request for MATLAB Code of Elephant Search Algorithm (ESA)
Hi everyone,
I’m looking for a MATLAB implementation of the Elephant Search Algorithm (ESA) for optimization problems. I couldn’t find an existing code online, and I’d appreciate it if someone could share a working implementation or guide me on how to code it based on its original paper.
If anyone has experience implementing ESA in MATLAB or can point me to a useful resource, I’d be very grateful.
Thanks in advance!Hi everyone,
I’m looking for a MATLAB implementation of the Elephant Search Algorithm (ESA) for optimization problems. I couldn’t find an existing code online, and I’d appreciate it if someone could share a working implementation or guide me on how to code it based on its original paper.
If anyone has experience implementing ESA in MATLAB or can point me to a useful resource, I’d be very grateful.
Thanks in advance! Hi everyone,
I’m looking for a MATLAB implementation of the Elephant Search Algorithm (ESA) for optimization problems. I couldn’t find an existing code online, and I’d appreciate it if someone could share a working implementation or guide me on how to code it based on its original paper.
If anyone has experience implementing ESA in MATLAB or can point me to a useful resource, I’d be very grateful.
Thanks in advance! esa, elephant search algorithm, code MATLAB Answers — New Questions
how to fuse image
Dear all,
this is my code to view CT image by slice
P = zeros(256, 256, 72);
for K = 1 : 72
petname = sprintf(‘I4%03d.dcm’, K);
P(:,:,K) = dicomread(petname);
end
imshow3D(P)
then, this is my code for view SPECT image by slice,
Noted: all my 512 slice SPECT image stored in one file.
[spect map]=dicomread(‘128×128’);
info = dicominfo(‘128×128’);
gp=info.SliceThickness;
spect=(squeeze(spect));%smooth3
aa=size(spect);aa=aa(3);
imshow3D(spect);
Anybody can help me to fuse both SPECT and CT images?Dear all,
this is my code to view CT image by slice
P = zeros(256, 256, 72);
for K = 1 : 72
petname = sprintf(‘I4%03d.dcm’, K);
P(:,:,K) = dicomread(petname);
end
imshow3D(P)
then, this is my code for view SPECT image by slice,
Noted: all my 512 slice SPECT image stored in one file.
[spect map]=dicomread(‘128×128’);
info = dicominfo(‘128×128’);
gp=info.SliceThickness;
spect=(squeeze(spect));%smooth3
aa=size(spect);aa=aa(3);
imshow3D(spect);
Anybody can help me to fuse both SPECT and CT images? Dear all,
this is my code to view CT image by slice
P = zeros(256, 256, 72);
for K = 1 : 72
petname = sprintf(‘I4%03d.dcm’, K);
P(:,:,K) = dicomread(petname);
end
imshow3D(P)
then, this is my code for view SPECT image by slice,
Noted: all my 512 slice SPECT image stored in one file.
[spect map]=dicomread(‘128×128’);
info = dicominfo(‘128×128’);
gp=info.SliceThickness;
spect=(squeeze(spect));%smooth3
aa=size(spect);aa=aa(3);
imshow3D(spect);
Anybody can help me to fuse both SPECT and CT images? image processing, image analysis, image segmentation MATLAB Answers — New Questions
Is the pny GeForce rtx 3090 supported by the deep learning toolbox?
Is the pny GeForce rtx 3090 supported by the deep learning toolbox?Is the pny GeForce rtx 3090 supported by the deep learning toolbox? Is the pny GeForce rtx 3090 supported by the deep learning toolbox? pny geforce MATLAB Answers — New Questions
make “plot” visualization more visible
I have 5 plots in a figure. One plot has a lot more points (gray color in the figure with about 4000 points) and hence while other a few hundred. I put the color for the larger set curve to gray in order to make the other 4 plots (shown in green, orange, and purple) more visible (following script). I also made the ‘Linewidth’ for gray curve to 0.5. Are the any suggestions to make the other 4 curves (shown as orange, green, red, and purple) more visible?
newcolors = [0.83 0.14 0.14
1.00 0.54 0.00
0.47 0.25 0.80
0.25 0.80 0.54
0.7 0.7 0.7];
colororder(newcolors)I have 5 plots in a figure. One plot has a lot more points (gray color in the figure with about 4000 points) and hence while other a few hundred. I put the color for the larger set curve to gray in order to make the other 4 plots (shown in green, orange, and purple) more visible (following script). I also made the ‘Linewidth’ for gray curve to 0.5. Are the any suggestions to make the other 4 curves (shown as orange, green, red, and purple) more visible?
newcolors = [0.83 0.14 0.14
1.00 0.54 0.00
0.47 0.25 0.80
0.25 0.80 0.54
0.7 0.7 0.7];
colororder(newcolors) I have 5 plots in a figure. One plot has a lot more points (gray color in the figure with about 4000 points) and hence while other a few hundred. I put the color for the larger set curve to gray in order to make the other 4 plots (shown in green, orange, and purple) more visible (following script). I also made the ‘Linewidth’ for gray curve to 0.5. Are the any suggestions to make the other 4 curves (shown as orange, green, red, and purple) more visible?
newcolors = [0.83 0.14 0.14
1.00 0.54 0.00
0.47 0.25 0.80
0.25 0.80 0.54
0.7 0.7 0.7];
colororder(newcolors) plot, visualization MATLAB Answers — New Questions
2022 or later version giving error when compiling model for nucleo board
using nucleo h743zi2 board working fine with matlaab version 2020 but when compling in later version gives error when using some analog pins on the boardusing nucleo h743zi2 board working fine with matlaab version 2020 but when compling in later version gives error when using some analog pins on the board using nucleo h743zi2 board working fine with matlaab version 2020 but when compling in later version gives error when using some analog pins on the board stm, nucleo MATLAB Answers — New Questions
why I cant use velodyneFileReader
hello here,
I am new to this velodyne toolbox, and I am try using velodyneFileReader to read my recorded PCAP file (around 9GB).
the code is as follows
veloReader = velodyneFileReader(‘velodyne.pcap’,’VLP16′)
but it gives me
vision.internal.VelodyneFileReader/open
Error reading packet from PCAP file bogus savefile header.
velodyneFileReader (line 269)
tmpReturnStruct = open(this.VelodyneFileReaderObj, this.FileName,…
rosbagtest (line 4)
veloReader = velodyneFileReader(‘velodyne.pcap’,’VLP16′)
I have tried this command with my other PCAP files (up to 1GB), they were all fine, so I am wondering if there is a size limit with this command or some other issues, thanks.hello here,
I am new to this velodyne toolbox, and I am try using velodyneFileReader to read my recorded PCAP file (around 9GB).
the code is as follows
veloReader = velodyneFileReader(‘velodyne.pcap’,’VLP16′)
but it gives me
vision.internal.VelodyneFileReader/open
Error reading packet from PCAP file bogus savefile header.
velodyneFileReader (line 269)
tmpReturnStruct = open(this.VelodyneFileReaderObj, this.FileName,…
rosbagtest (line 4)
veloReader = velodyneFileReader(‘velodyne.pcap’,’VLP16′)
I have tried this command with my other PCAP files (up to 1GB), they were all fine, so I am wondering if there is a size limit with this command or some other issues, thanks. hello here,
I am new to this velodyne toolbox, and I am try using velodyneFileReader to read my recorded PCAP file (around 9GB).
the code is as follows
veloReader = velodyneFileReader(‘velodyne.pcap’,’VLP16′)
but it gives me
vision.internal.VelodyneFileReader/open
Error reading packet from PCAP file bogus savefile header.
velodyneFileReader (line 269)
tmpReturnStruct = open(this.VelodyneFileReaderObj, this.FileName,…
rosbagtest (line 4)
veloReader = velodyneFileReader(‘velodyne.pcap’,’VLP16′)
I have tried this command with my other PCAP files (up to 1GB), they were all fine, so I am wondering if there is a size limit with this command or some other issues, thanks. velodyne, lidar MATLAB Answers — New Questions
Debunking Six Myths of Cloud Native Security
The promise of cloud native applications lies in their ability to provide enhanced agility, scalability, and resilience, perfectly aligning with the digital transformation needs of today’s enterprises. However, as we navigate this transformation, cloud native application security is often surrounded by myths and misconceptions. Understanding these myths and how they are being addressed is crucial for organizations to secure their environments effectively.
The promise of cloud native applications lies in their ability to provide enhanced agility, scalability, and resilience, perfectly aligning with the digital transformation needs of today’s enterprises. However, as we navigate this transformation, cloud native application security is often surrounded by myths and misconceptions. Understanding these myths and how they are being addressed is crucial for organizations to secure their environments effectively.Read More
Matlab using in sSimulink
Hi,
I want to connect PCM_port to pos CELL input model created by simple code (file.m)
I can not understand why it does not works well.
results in attached pictures
Required result of pic – connection required.
Thank for support.
the following code:
pen_system(‘Module_arc’)
mdl = ‘Module_arc’;
bat_rec_model = find_system(mdl,’FindAll’,’on’,’Name’,’Module_arc’);
%%% add Cell – basic CELL_unit:
for i=1:2 %% set two columns
colPos = 200; %% spaces between columns
for v=1:2 %% loop for 13 cells per column
nl=num2str(v + 2*(i-1));
if i==1
AddCell(v) = add_block(‘CELL_Unit/CELL 1’, [mdl,’/CELL ‘,nl]);
else
AddCell(v) = add_block(‘CELL_Unit2/CELL 1’, [mdl,’/CELL ‘,nl]);
end
posc = get(AddCell(v),’Position’);
set(AddCell(v),’Position’,posc + [100+(i-1)*colPos 120*(v-1)-45 100+(i-1)*colPos 120*(v-1)-45])
PH_AddCell{v}=get(AddCell(v),’PortHandles’);
%%% connect minus to plus ports:
if v>1
add_line(mdl,PH_AddCell{v-1}.LConn(2),PH_AddCell{v}.LConn(1),’Autorouting’,’on’);
end
end
end
%%% connect the positive port to first CELL (Cell 1):
Plus_2_Cell = find_system(mdl,’LookUnderMasks’,’All’,’FindAll’,’on’,’Name’,’POS’);
PH_plus2Cell=get(Plus_2_Cell,’PortHandles’);
Pos_port= add_line(mdl,PH_plus2Cell.RConn,PH_AddCell{1}.LConn(1,1), ‘Autorouting’,’on’);Hi,
I want to connect PCM_port to pos CELL input model created by simple code (file.m)
I can not understand why it does not works well.
results in attached pictures
Required result of pic – connection required.
Thank for support.
the following code:
pen_system(‘Module_arc’)
mdl = ‘Module_arc’;
bat_rec_model = find_system(mdl,’FindAll’,’on’,’Name’,’Module_arc’);
%%% add Cell – basic CELL_unit:
for i=1:2 %% set two columns
colPos = 200; %% spaces between columns
for v=1:2 %% loop for 13 cells per column
nl=num2str(v + 2*(i-1));
if i==1
AddCell(v) = add_block(‘CELL_Unit/CELL 1’, [mdl,’/CELL ‘,nl]);
else
AddCell(v) = add_block(‘CELL_Unit2/CELL 1’, [mdl,’/CELL ‘,nl]);
end
posc = get(AddCell(v),’Position’);
set(AddCell(v),’Position’,posc + [100+(i-1)*colPos 120*(v-1)-45 100+(i-1)*colPos 120*(v-1)-45])
PH_AddCell{v}=get(AddCell(v),’PortHandles’);
%%% connect minus to plus ports:
if v>1
add_line(mdl,PH_AddCell{v-1}.LConn(2),PH_AddCell{v}.LConn(1),’Autorouting’,’on’);
end
end
end
%%% connect the positive port to first CELL (Cell 1):
Plus_2_Cell = find_system(mdl,’LookUnderMasks’,’All’,’FindAll’,’on’,’Name’,’POS’);
PH_plus2Cell=get(Plus_2_Cell,’PortHandles’);
Pos_port= add_line(mdl,PH_plus2Cell.RConn,PH_AddCell{1}.LConn(1,1), ‘Autorouting’,’on’); Hi,
I want to connect PCM_port to pos CELL input model created by simple code (file.m)
I can not understand why it does not works well.
results in attached pictures
Required result of pic – connection required.
Thank for support.
the following code:
pen_system(‘Module_arc’)
mdl = ‘Module_arc’;
bat_rec_model = find_system(mdl,’FindAll’,’on’,’Name’,’Module_arc’);
%%% add Cell – basic CELL_unit:
for i=1:2 %% set two columns
colPos = 200; %% spaces between columns
for v=1:2 %% loop for 13 cells per column
nl=num2str(v + 2*(i-1));
if i==1
AddCell(v) = add_block(‘CELL_Unit/CELL 1’, [mdl,’/CELL ‘,nl]);
else
AddCell(v) = add_block(‘CELL_Unit2/CELL 1’, [mdl,’/CELL ‘,nl]);
end
posc = get(AddCell(v),’Position’);
set(AddCell(v),’Position’,posc + [100+(i-1)*colPos 120*(v-1)-45 100+(i-1)*colPos 120*(v-1)-45])
PH_AddCell{v}=get(AddCell(v),’PortHandles’);
%%% connect minus to plus ports:
if v>1
add_line(mdl,PH_AddCell{v-1}.LConn(2),PH_AddCell{v}.LConn(1),’Autorouting’,’on’);
end
end
end
%%% connect the positive port to first CELL (Cell 1):
Plus_2_Cell = find_system(mdl,’LookUnderMasks’,’All’,’FindAll’,’on’,’Name’,’POS’);
PH_plus2Cell=get(Plus_2_Cell,’PortHandles’);
Pos_port= add_line(mdl,PH_plus2Cell.RConn,PH_AddCell{1}.LConn(1,1), ‘Autorouting’,’on’); matlab, simulink MATLAB Answers — New Questions
Simulink/ Stateflow crash when running model in accelerator mode on 2020B – failed assertion
Hello
I have a model which references many other models, this runs fine when I simulate in normal mode, but when I choose accelerator mode one referenced model crashes and therefore the top model willl not run. I get the following error. No idea what it means. The particular model handles a ton of inputs, I tend to think that has something to do with it. Any pointers appreciated!!
Cheers
Richard
failed assertion in b:matlabtoolboxstateflowsrcstateflowfsmde.cpp at line 167:
mxGetLength(prhs[0]) == 1
[ 0] 0x00007ff871985c0d C:UsersmeDocuments2021binwin64stateflow.dll+14048269 SF::mx2str_write_str+00036989
[ 1] 0x00007ff8716287e6 C:UsersmeDocuments2021binwin64stateflow.dll+10520550 SF::sf_mexFunction+00002406
[ 2] 0x00007ff871627e95 C:UsersmeDocuments2021binwin64stateflow.dll+10518165 SF::sf_mexFunction+00000021
[ 3] 0x00007ff8e480a289 C:UsersmeDocuments2021binwin64libmex.dll+00565897 mexfile::Inspector::needs_upgrade+00015977
[ 4] 0x00007ff8e480b67b C:UsersmeDocuments2021binwin64libmex.dll+00571003 MexRetrieveVersion+00003579
[ 5] 0x00007ff8e480b9b4 C:UsersmeDocuments2021binwin64libmex.dll+00571828 MexRetrieveVersion+00004404
[ 6] 0x00007ff8e47ec209 C:UsersmeDocuments2021binwin64libmex.dll+00442889 mexUnlock_800+00023993
[ 7] 0x00007ff8e4dc26c1 binwin64pgom_dispatcher.dll+00665281 Mfh_file::dispatch_file_common+00000165
[ 8] 0x00007ff8e4dc1d62 binwin64pgom_dispatcher.dll+00662882 Mfh_error::lookup+00006498
[ 9] 0x00007ff8e4dc2615 binwin64pgom_dispatcher.dll+00665109 Mfh_file::dispatch+00000045
[ 10] 0x00007ff8e1ec8208 binwin64pgom_lxe.dll+00557576 CGGBuiltinImpl+00181784
[ 11] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 12] 0x00007ff8e1fc7861 binwin64pgom_lxe.dll+01603681 mwboost::serialization::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >+00649937
[ 13] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[ 14] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[ 15] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[ 16] 0x00007ff8e1eabeb3 binwin64pgom_lxe.dll+00442035 CGGBuiltinImpl+00066243
[ 17] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[ 18] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 19] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 20] 0x00007ff8e3a3fcc2 binwin64pgom_lxe.dll+29359298 mwboost::archive::detail::pointer_oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::MatlabIrTree>::save_object_ptr+00532802
[ 21] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[ 22] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[ 23] 0x00007ff8e4abc97c binwin64pgom_interpreter.dll+00051580 inCallFcn+00000084
[ 24] 0x00007ff8d0f34910 C:UsersmeDocuments2021binwin64libmwsl_services.dll+02509072 slsvFEVAL+00001248
[ 25] 0x00007ff87162839e C:UsersmeDocuments2021binwin64stateflow.dll+10519454 SF::sf_mexFunction+00001310
[ 26] 0x00007ff871985c0d C:UsersmeDocuments2021binwin64stateflow.dll+14048269 SF::mx2str_write_str+00036989
[ 27] 0x00007ff8716287e6 C:UsersmeDocuments2021binwin64stateflow.dll+10520550 SF::sf_mexFunction+00002406
[ 28] 0x00007ff871627e95 C:UsersmeDocuments2021binwin64stateflow.dll+10518165 SF::sf_mexFunction+00000021
[ 29] 0x00007ff8e480a289 C:UsersmeDocuments2021binwin64libmex.dll+00565897 mexfile::Inspector::needs_upgrade+00015977
[ 30] 0x00007ff8e480b67b C:UsersmeDocuments2021binwin64libmex.dll+00571003 MexRetrieveVersion+00003579
[ 31] 0x00007ff8e480b9b4 C:UsersmeDocuments2021binwin64libmex.dll+00571828 MexRetrieveVersion+00004404
[ 32] 0x00007ff8e47ec209 C:UsersmeDocuments2021binwin64libmex.dll+00442889 mexUnlock_800+00023993
[ 33] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 34] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 35] 0x00007ff8e1ee456a binwin64pgom_lxe.dll+00673130 CGGBuiltinImpl+00297338
[ 36] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[ 37] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[ 38] 0x00007ff8e4abc97c binwin64pgom_interpreter.dll+00051580 inCallFcn+00000084
[ 39] 0x00007ff8d0f121cc C:UsersmeDocuments2021binwin64libmwsl_services.dll+02367948 slsv::mxw::WarningHandler::operator=+00006620
[ 40] 0x00007ff8d0f2542c C:UsersmeDocuments2021binwin64libmwsl_services.dll+02446380 CMatlabCommand::execute+00000364
[ 41] 0x00007ff8d0f34a9d C:UsersmeDocuments2021binwin64libmwsl_services.dll+02509469 slsvFEVAL_NoThrow+00000285
[ 42] 0x00007ff8d0f34b30 C:UsersmeDocuments2021binwin64libmwsl_services.dll+02509616 slsvFEVAL_NoThrow+00000064
[ 43] 0x00007ff8b7c1c2c7 C:UsersmeDocuments2021binwin64sl_graphical_classes.dll+02540231 NotifyStateflowNamedEventRecursively+00000951
[ 44] 0x00007ff8a4d6fb5f C:UsersmeDocuments2021binwin64sl_compile.dll+03799903 SlCallSysSort::operator=+00005199
[ 45] 0x00007ff8a4db4762 C:UsersmeDocuments2021binwin64sl_compile.dll+04081506 VarHiddenConnectionInfo::setNIForRemoval+00012818
[ 46] 0x00007ff8a4d2b204 C:UsersmeDocuments2021binwin64sl_compile.dll+03518980 slcomp::data_transfer::DataTransferSpec::setIntegrity+00010596
[ 47] 0x00007ff8a4d1a345 C:UsersmeDocuments2021binwin64sl_compile.dll+03449669 SLCompDiagramImpl+00000709
[ 48] 0x00007ff8ba403e8c C:UsersmeDocuments2021binwin64sl_link_bd.dll+01064588 slink::CreateSlModelForLink+00000780
[ 49] 0x00007ff8ba403bac C:UsersmeDocuments2021binwin64sl_link_bd.dll+01063852 slink::CreateSlModelForLink+00000044
[ 50] 0x00007ff8bb5e6a49 C:UsersmeDocuments2021binwin64libmwsimulink.dll+10709577 SLDynamicEnumType::isLocked+00057273
[ 51] 0x00007ff8bb5f297f C:UsersmeDocuments2021binwin64libmwsimulink.dll+10758527 SLDynamicEnumType::isLocked+00106223
[ 52] 0x00007ff8bb323ed0 C:UsersmeDocuments2021binwin64libmwsimulink.dll+07814864 slFullRTWGen+00000368
[ 53] 0x00007ff8ef8a0cbb C:UsersmeDocuments2021binwin64builtin.dll+00068795 MathWorks::builtin::BuiltinFcn::invoke+00001403
[ 54] 0x00007ff8e4d42156 binwin64pgom_dispatcher.dll+00139606 Mfh_MATLAB_fn_impl::dispatch_mf_with_reuse+00000158
[ 55] 0x00007ff8e4d32053 binwin64pgom_dispatcher.dll+00073811 Mfh_MATLAB_fn_impl::dispatch+00000723
[ 56] 0x00007ff8e4d31dad binwin64pgom_dispatcher.dll+00073133 Mfh_MATLAB_fn_impl::dispatch+00000045
[ 57] 0x00007ff8e1ec8208 binwin64pgom_lxe.dll+00557576 CGGBuiltinImpl+00181784
[ 58] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 59] 0x00007ff8e1fc7861 binwin64pgom_lxe.dll+01603681 mwboost::serialization::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >+00649937
[ 60] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[ 61] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[ 62] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[ 63] 0x00007ff8e1eabeb3 binwin64pgom_lxe.dll+00442035 CGGBuiltinImpl+00066243
[ 64] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[ 65] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 66] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 67] 0x00007ff8db38744a binwin64pgomcos_impl.dll+00685130 mdIsA+00257770
[ 68] 0x00007ff8db3452cd binwin64pgomcos_impl.dll+00414413
[ 69] 0x00007ff8db358c9d binwin64pgomcos_impl.dll+00494749 mdIsA+00067389
[ 70] 0x00007ff8e4d33157 binwin64pgom_dispatcher.dll+00078167 Mfh_MATLAB_fn_impl::ensure_loading_of_load_module+00001895
[ 71] 0x00007ff8e4d3331d binwin64pgom_dispatcher.dll+00078621 Mfh_MATLAB_fn_impl::dispatch_with_reuse+00000045
[ 72] 0x00007ff8e1ee3d17 binwin64pgom_lxe.dll+00670999 CGGBuiltinImpl+00295207
[ 73] 0x00007ff8e1eaa56a binwin64pgom_lxe.dll+00435562 CGGBuiltinImpl+00059770
[ 74] 0x00007ff8e1ed7701 binwin64pgom_lxe.dll+00620289 CGGBuiltinImpl+00244497
[ 75] 0x00007ff8e1ed762b binwin64pgom_lxe.dll+00620075 CGGBuiltinImpl+00244283
[ 76] 0x00007ff8e1ed7587 binwin64pgom_lxe.dll+00619911 CGGBuiltinImpl+00244119
[ 77] 0x00007ff8e1ed731f binwin64pgom_lxe.dll+00619295 CGGBuiltinImpl+00243503
[ 78] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 79] 0x00007ff8e200a4dd binwin64pgom_lxe.dll+01877213 mwboost::archive::detail::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>+00054677
[ 80] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[ 81] 0x00007ff8e2000494 binwin64pgom_lxe.dll+01836180 mwboost::archive::detail::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>+00013644
[ 82] 0x00007ff8e1ea9747 binwin64pgom_lxe.dll+00431943 CGGBuiltinImpl+00056151
[ 83] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[ 84] 0x00007ff8e1eabeb3 binwin64pgom_lxe.dll+00442035 CGGBuiltinImpl+00066243
[ 85] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[ 86] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 87] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 88] 0x00007ff8e1ee456a binwin64pgom_lxe.dll+00673130 CGGBuiltinImpl+00297338
[ 89] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[ 90] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[ 91] 0x00007ff8e4ab9991 binwin64pgom_interpreter.dll+00039313 inFullFevalFcn+00000369
[ 92] 0x00007ff8ef8a0cbb C:UsersS60415Documents2021binwin64builtin.dll+00068795 MathWorks::builtin::BuiltinFcn::invoke+00001403
[ 93] 0x00007ff8e4d42156 binwin64pgom_dispatcher.dll+00139606 Mfh_MATLAB_fn_impl::dispatch_mf_with_reuse+00000158
[ 94] 0x00007ff8e4d32053 binwin64pgom_dispatcher.dll+00073811 Mfh_MATLAB_fn_impl::dispatch+00000723
[ 95] 0x00007ff8e4d31dad binwin64pgom_dispatcher.dll+00073133 Mfh_MATLAB_fn_impl::dispatch+00000045
[ 96] 0x00007ff8e1ec8208 binwin64pgom_lxe.dll+00557576 CGGBuiltinImpl+00181784
[ 97] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 98] 0x00007ff8e1fc7861 binwin64pgom_lxe.dll+01603681 mwboost::serialization::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >+00649937
[ 99] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[100] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[101] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[102] 0x00007ff8e1eac19c binwin64pgom_lxe.dll+00442780 CGGBuiltinImpl+00066988
[103] 0x00007ff8e1ed99aa binwin64pgom_lxe.dll+00629162 CGGBuiltinImpl+00253370
[104] 0x00007ff8e1ed974f binwin64pgom_lxe.dll+00628559 CGGBuiltinImpl+00252767
[105] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[106] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[107] 0x00007ff8e4ab9991 binwin64pgom_interpreter.dll+00039313 inFullFevalFcn+00000369
[108] 0x00007ff8ef8a0cbb C:UsersS60415Documents2021binwin64builtin.dll+00068795 MathWorks::builtin::BuiltinFcn::invoke+00001403
[109] 0x00007ff8e4d42156 binwin64pgom_dispatcher.dll+00139606 Mfh_MATLAB_fn_impl::dispatch_mf_with_reuse+00000158
[110] 0x00007ff8e4d32053 binwin64pgom_dispatcher.dll+00073811 Mfh_MATLAB_fn_impl::dispatch+00000723
[111] 0x00007ff8e4d31dad binwin64pgom_dispatcher.dll+00073133 Mfh_MATLAB_fn_impl::dispatch+00000045
[112] 0x00007ff8e1ec875f binwin64pgom_lxe.dll+00558943 CGGBuiltinImpl+00183151
[113] 0x00007ff8e1feba76 binwin64pgom_lxe.dll+01751670 MathWorks::lxe::ShutdownLxeEngine+00033418
[114] 0x00007ff8e1feb8e3 binwin64pgom_lxe.dll+01751267 MathWorks::lxe::ShutdownLxeEngine+00033015
[115] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[116] 0x00007ff8e20053a1 binwin64pgom_lxe.dll+01856417 mwboost::archive::detail::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>+00033881
[117] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[118] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[119] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[120] 0x00007ff8e1eac19c binwin64pgom_lxe.dll+00442780 CGGBuiltinImpl+00066988
[121] 0x00007ff8e1ed7c41 binwin64pgom_lxe.dll+00621633 CGGBuiltinImpl+00245841
[122] 0x00007ff8e1eac010 binwin64pgom_lxe.dll+00442384 CGGBuiltinImpl+00066592
[123] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[124] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[125] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[126] 0x00007ff8e3a3fcc2 binwin64pgom_lxe.dll+29359298 mwboost::archive::detail::pointer_oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::MatlabIrTree>::save_object_ptr+00532802
[127] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
MATLAB stack trace:
> In perform_model_reference_error_checks>error_check_machine_data_events (line 28)
In perform_model_reference_error_checks (line 18)
In autobuild_kernel>autobuild_local (line 127)
In autobuild_kernel (line 53)
In autobuild_driver (line 117)
In slsf (line 129)
In tlc_c
In coder.internal/ModelBuilder/buildProcedure (line 823)
In coder.internal/ModelBuilder/make_rtw (line 168)
In coder.internal.ModelCodegenMgr/make_rtw (line 8)
In make_rtw (line 20)
In build_target
In build_target
In build_model_reference_target
In updateMdlRefTarget
In slprivate
In coder.serial.hBuildModelRefs
In coder.serial.buildModelRefs
In update_model_reference_targets
In update_model_reference_targets
In update_model_reference_targets
In slbuild_private
In slbuild_private
In slprivate
mxGetLength(prhs[0]) == 1
Component:Stateflow | Category:Assertion error
### Build procedure for ACIN_HANDLER aborted due to an error.Hello
I have a model which references many other models, this runs fine when I simulate in normal mode, but when I choose accelerator mode one referenced model crashes and therefore the top model willl not run. I get the following error. No idea what it means. The particular model handles a ton of inputs, I tend to think that has something to do with it. Any pointers appreciated!!
Cheers
Richard
failed assertion in b:matlabtoolboxstateflowsrcstateflowfsmde.cpp at line 167:
mxGetLength(prhs[0]) == 1
[ 0] 0x00007ff871985c0d C:UsersmeDocuments2021binwin64stateflow.dll+14048269 SF::mx2str_write_str+00036989
[ 1] 0x00007ff8716287e6 C:UsersmeDocuments2021binwin64stateflow.dll+10520550 SF::sf_mexFunction+00002406
[ 2] 0x00007ff871627e95 C:UsersmeDocuments2021binwin64stateflow.dll+10518165 SF::sf_mexFunction+00000021
[ 3] 0x00007ff8e480a289 C:UsersmeDocuments2021binwin64libmex.dll+00565897 mexfile::Inspector::needs_upgrade+00015977
[ 4] 0x00007ff8e480b67b C:UsersmeDocuments2021binwin64libmex.dll+00571003 MexRetrieveVersion+00003579
[ 5] 0x00007ff8e480b9b4 C:UsersmeDocuments2021binwin64libmex.dll+00571828 MexRetrieveVersion+00004404
[ 6] 0x00007ff8e47ec209 C:UsersmeDocuments2021binwin64libmex.dll+00442889 mexUnlock_800+00023993
[ 7] 0x00007ff8e4dc26c1 binwin64pgom_dispatcher.dll+00665281 Mfh_file::dispatch_file_common+00000165
[ 8] 0x00007ff8e4dc1d62 binwin64pgom_dispatcher.dll+00662882 Mfh_error::lookup+00006498
[ 9] 0x00007ff8e4dc2615 binwin64pgom_dispatcher.dll+00665109 Mfh_file::dispatch+00000045
[ 10] 0x00007ff8e1ec8208 binwin64pgom_lxe.dll+00557576 CGGBuiltinImpl+00181784
[ 11] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 12] 0x00007ff8e1fc7861 binwin64pgom_lxe.dll+01603681 mwboost::serialization::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >+00649937
[ 13] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[ 14] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[ 15] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[ 16] 0x00007ff8e1eabeb3 binwin64pgom_lxe.dll+00442035 CGGBuiltinImpl+00066243
[ 17] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[ 18] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 19] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 20] 0x00007ff8e3a3fcc2 binwin64pgom_lxe.dll+29359298 mwboost::archive::detail::pointer_oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::MatlabIrTree>::save_object_ptr+00532802
[ 21] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[ 22] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[ 23] 0x00007ff8e4abc97c binwin64pgom_interpreter.dll+00051580 inCallFcn+00000084
[ 24] 0x00007ff8d0f34910 C:UsersmeDocuments2021binwin64libmwsl_services.dll+02509072 slsvFEVAL+00001248
[ 25] 0x00007ff87162839e C:UsersmeDocuments2021binwin64stateflow.dll+10519454 SF::sf_mexFunction+00001310
[ 26] 0x00007ff871985c0d C:UsersmeDocuments2021binwin64stateflow.dll+14048269 SF::mx2str_write_str+00036989
[ 27] 0x00007ff8716287e6 C:UsersmeDocuments2021binwin64stateflow.dll+10520550 SF::sf_mexFunction+00002406
[ 28] 0x00007ff871627e95 C:UsersmeDocuments2021binwin64stateflow.dll+10518165 SF::sf_mexFunction+00000021
[ 29] 0x00007ff8e480a289 C:UsersmeDocuments2021binwin64libmex.dll+00565897 mexfile::Inspector::needs_upgrade+00015977
[ 30] 0x00007ff8e480b67b C:UsersmeDocuments2021binwin64libmex.dll+00571003 MexRetrieveVersion+00003579
[ 31] 0x00007ff8e480b9b4 C:UsersmeDocuments2021binwin64libmex.dll+00571828 MexRetrieveVersion+00004404
[ 32] 0x00007ff8e47ec209 C:UsersmeDocuments2021binwin64libmex.dll+00442889 mexUnlock_800+00023993
[ 33] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 34] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 35] 0x00007ff8e1ee456a binwin64pgom_lxe.dll+00673130 CGGBuiltinImpl+00297338
[ 36] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[ 37] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[ 38] 0x00007ff8e4abc97c binwin64pgom_interpreter.dll+00051580 inCallFcn+00000084
[ 39] 0x00007ff8d0f121cc C:UsersmeDocuments2021binwin64libmwsl_services.dll+02367948 slsv::mxw::WarningHandler::operator=+00006620
[ 40] 0x00007ff8d0f2542c C:UsersmeDocuments2021binwin64libmwsl_services.dll+02446380 CMatlabCommand::execute+00000364
[ 41] 0x00007ff8d0f34a9d C:UsersmeDocuments2021binwin64libmwsl_services.dll+02509469 slsvFEVAL_NoThrow+00000285
[ 42] 0x00007ff8d0f34b30 C:UsersmeDocuments2021binwin64libmwsl_services.dll+02509616 slsvFEVAL_NoThrow+00000064
[ 43] 0x00007ff8b7c1c2c7 C:UsersmeDocuments2021binwin64sl_graphical_classes.dll+02540231 NotifyStateflowNamedEventRecursively+00000951
[ 44] 0x00007ff8a4d6fb5f C:UsersmeDocuments2021binwin64sl_compile.dll+03799903 SlCallSysSort::operator=+00005199
[ 45] 0x00007ff8a4db4762 C:UsersmeDocuments2021binwin64sl_compile.dll+04081506 VarHiddenConnectionInfo::setNIForRemoval+00012818
[ 46] 0x00007ff8a4d2b204 C:UsersmeDocuments2021binwin64sl_compile.dll+03518980 slcomp::data_transfer::DataTransferSpec::setIntegrity+00010596
[ 47] 0x00007ff8a4d1a345 C:UsersmeDocuments2021binwin64sl_compile.dll+03449669 SLCompDiagramImpl+00000709
[ 48] 0x00007ff8ba403e8c C:UsersmeDocuments2021binwin64sl_link_bd.dll+01064588 slink::CreateSlModelForLink+00000780
[ 49] 0x00007ff8ba403bac C:UsersmeDocuments2021binwin64sl_link_bd.dll+01063852 slink::CreateSlModelForLink+00000044
[ 50] 0x00007ff8bb5e6a49 C:UsersmeDocuments2021binwin64libmwsimulink.dll+10709577 SLDynamicEnumType::isLocked+00057273
[ 51] 0x00007ff8bb5f297f C:UsersmeDocuments2021binwin64libmwsimulink.dll+10758527 SLDynamicEnumType::isLocked+00106223
[ 52] 0x00007ff8bb323ed0 C:UsersmeDocuments2021binwin64libmwsimulink.dll+07814864 slFullRTWGen+00000368
[ 53] 0x00007ff8ef8a0cbb C:UsersmeDocuments2021binwin64builtin.dll+00068795 MathWorks::builtin::BuiltinFcn::invoke+00001403
[ 54] 0x00007ff8e4d42156 binwin64pgom_dispatcher.dll+00139606 Mfh_MATLAB_fn_impl::dispatch_mf_with_reuse+00000158
[ 55] 0x00007ff8e4d32053 binwin64pgom_dispatcher.dll+00073811 Mfh_MATLAB_fn_impl::dispatch+00000723
[ 56] 0x00007ff8e4d31dad binwin64pgom_dispatcher.dll+00073133 Mfh_MATLAB_fn_impl::dispatch+00000045
[ 57] 0x00007ff8e1ec8208 binwin64pgom_lxe.dll+00557576 CGGBuiltinImpl+00181784
[ 58] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 59] 0x00007ff8e1fc7861 binwin64pgom_lxe.dll+01603681 mwboost::serialization::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >+00649937
[ 60] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[ 61] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[ 62] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[ 63] 0x00007ff8e1eabeb3 binwin64pgom_lxe.dll+00442035 CGGBuiltinImpl+00066243
[ 64] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[ 65] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 66] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 67] 0x00007ff8db38744a binwin64pgomcos_impl.dll+00685130 mdIsA+00257770
[ 68] 0x00007ff8db3452cd binwin64pgomcos_impl.dll+00414413
[ 69] 0x00007ff8db358c9d binwin64pgomcos_impl.dll+00494749 mdIsA+00067389
[ 70] 0x00007ff8e4d33157 binwin64pgom_dispatcher.dll+00078167 Mfh_MATLAB_fn_impl::ensure_loading_of_load_module+00001895
[ 71] 0x00007ff8e4d3331d binwin64pgom_dispatcher.dll+00078621 Mfh_MATLAB_fn_impl::dispatch_with_reuse+00000045
[ 72] 0x00007ff8e1ee3d17 binwin64pgom_lxe.dll+00670999 CGGBuiltinImpl+00295207
[ 73] 0x00007ff8e1eaa56a binwin64pgom_lxe.dll+00435562 CGGBuiltinImpl+00059770
[ 74] 0x00007ff8e1ed7701 binwin64pgom_lxe.dll+00620289 CGGBuiltinImpl+00244497
[ 75] 0x00007ff8e1ed762b binwin64pgom_lxe.dll+00620075 CGGBuiltinImpl+00244283
[ 76] 0x00007ff8e1ed7587 binwin64pgom_lxe.dll+00619911 CGGBuiltinImpl+00244119
[ 77] 0x00007ff8e1ed731f binwin64pgom_lxe.dll+00619295 CGGBuiltinImpl+00243503
[ 78] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 79] 0x00007ff8e200a4dd binwin64pgom_lxe.dll+01877213 mwboost::archive::detail::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>+00054677
[ 80] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[ 81] 0x00007ff8e2000494 binwin64pgom_lxe.dll+01836180 mwboost::archive::detail::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>+00013644
[ 82] 0x00007ff8e1ea9747 binwin64pgom_lxe.dll+00431943 CGGBuiltinImpl+00056151
[ 83] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[ 84] 0x00007ff8e1eabeb3 binwin64pgom_lxe.dll+00442035 CGGBuiltinImpl+00066243
[ 85] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[ 86] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 87] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 88] 0x00007ff8e1ee456a binwin64pgom_lxe.dll+00673130 CGGBuiltinImpl+00297338
[ 89] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[ 90] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[ 91] 0x00007ff8e4ab9991 binwin64pgom_interpreter.dll+00039313 inFullFevalFcn+00000369
[ 92] 0x00007ff8ef8a0cbb C:UsersS60415Documents2021binwin64builtin.dll+00068795 MathWorks::builtin::BuiltinFcn::invoke+00001403
[ 93] 0x00007ff8e4d42156 binwin64pgom_dispatcher.dll+00139606 Mfh_MATLAB_fn_impl::dispatch_mf_with_reuse+00000158
[ 94] 0x00007ff8e4d32053 binwin64pgom_dispatcher.dll+00073811 Mfh_MATLAB_fn_impl::dispatch+00000723
[ 95] 0x00007ff8e4d31dad binwin64pgom_dispatcher.dll+00073133 Mfh_MATLAB_fn_impl::dispatch+00000045
[ 96] 0x00007ff8e1ec8208 binwin64pgom_lxe.dll+00557576 CGGBuiltinImpl+00181784
[ 97] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 98] 0x00007ff8e1fc7861 binwin64pgom_lxe.dll+01603681 mwboost::serialization::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >+00649937
[ 99] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[100] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[101] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[102] 0x00007ff8e1eac19c binwin64pgom_lxe.dll+00442780 CGGBuiltinImpl+00066988
[103] 0x00007ff8e1ed99aa binwin64pgom_lxe.dll+00629162 CGGBuiltinImpl+00253370
[104] 0x00007ff8e1ed974f binwin64pgom_lxe.dll+00628559 CGGBuiltinImpl+00252767
[105] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[106] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[107] 0x00007ff8e4ab9991 binwin64pgom_interpreter.dll+00039313 inFullFevalFcn+00000369
[108] 0x00007ff8ef8a0cbb C:UsersS60415Documents2021binwin64builtin.dll+00068795 MathWorks::builtin::BuiltinFcn::invoke+00001403
[109] 0x00007ff8e4d42156 binwin64pgom_dispatcher.dll+00139606 Mfh_MATLAB_fn_impl::dispatch_mf_with_reuse+00000158
[110] 0x00007ff8e4d32053 binwin64pgom_dispatcher.dll+00073811 Mfh_MATLAB_fn_impl::dispatch+00000723
[111] 0x00007ff8e4d31dad binwin64pgom_dispatcher.dll+00073133 Mfh_MATLAB_fn_impl::dispatch+00000045
[112] 0x00007ff8e1ec875f binwin64pgom_lxe.dll+00558943 CGGBuiltinImpl+00183151
[113] 0x00007ff8e1feba76 binwin64pgom_lxe.dll+01751670 MathWorks::lxe::ShutdownLxeEngine+00033418
[114] 0x00007ff8e1feb8e3 binwin64pgom_lxe.dll+01751267 MathWorks::lxe::ShutdownLxeEngine+00033015
[115] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[116] 0x00007ff8e20053a1 binwin64pgom_lxe.dll+01856417 mwboost::archive::detail::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>+00033881
[117] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[118] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[119] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[120] 0x00007ff8e1eac19c binwin64pgom_lxe.dll+00442780 CGGBuiltinImpl+00066988
[121] 0x00007ff8e1ed7c41 binwin64pgom_lxe.dll+00621633 CGGBuiltinImpl+00245841
[122] 0x00007ff8e1eac010 binwin64pgom_lxe.dll+00442384 CGGBuiltinImpl+00066592
[123] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[124] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[125] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[126] 0x00007ff8e3a3fcc2 binwin64pgom_lxe.dll+29359298 mwboost::archive::detail::pointer_oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::MatlabIrTree>::save_object_ptr+00532802
[127] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
MATLAB stack trace:
> In perform_model_reference_error_checks>error_check_machine_data_events (line 28)
In perform_model_reference_error_checks (line 18)
In autobuild_kernel>autobuild_local (line 127)
In autobuild_kernel (line 53)
In autobuild_driver (line 117)
In slsf (line 129)
In tlc_c
In coder.internal/ModelBuilder/buildProcedure (line 823)
In coder.internal/ModelBuilder/make_rtw (line 168)
In coder.internal.ModelCodegenMgr/make_rtw (line 8)
In make_rtw (line 20)
In build_target
In build_target
In build_model_reference_target
In updateMdlRefTarget
In slprivate
In coder.serial.hBuildModelRefs
In coder.serial.buildModelRefs
In update_model_reference_targets
In update_model_reference_targets
In update_model_reference_targets
In slbuild_private
In slbuild_private
In slprivate
mxGetLength(prhs[0]) == 1
Component:Stateflow | Category:Assertion error
### Build procedure for ACIN_HANDLER aborted due to an error. Hello
I have a model which references many other models, this runs fine when I simulate in normal mode, but when I choose accelerator mode one referenced model crashes and therefore the top model willl not run. I get the following error. No idea what it means. The particular model handles a ton of inputs, I tend to think that has something to do with it. Any pointers appreciated!!
Cheers
Richard
failed assertion in b:matlabtoolboxstateflowsrcstateflowfsmde.cpp at line 167:
mxGetLength(prhs[0]) == 1
[ 0] 0x00007ff871985c0d C:UsersmeDocuments2021binwin64stateflow.dll+14048269 SF::mx2str_write_str+00036989
[ 1] 0x00007ff8716287e6 C:UsersmeDocuments2021binwin64stateflow.dll+10520550 SF::sf_mexFunction+00002406
[ 2] 0x00007ff871627e95 C:UsersmeDocuments2021binwin64stateflow.dll+10518165 SF::sf_mexFunction+00000021
[ 3] 0x00007ff8e480a289 C:UsersmeDocuments2021binwin64libmex.dll+00565897 mexfile::Inspector::needs_upgrade+00015977
[ 4] 0x00007ff8e480b67b C:UsersmeDocuments2021binwin64libmex.dll+00571003 MexRetrieveVersion+00003579
[ 5] 0x00007ff8e480b9b4 C:UsersmeDocuments2021binwin64libmex.dll+00571828 MexRetrieveVersion+00004404
[ 6] 0x00007ff8e47ec209 C:UsersmeDocuments2021binwin64libmex.dll+00442889 mexUnlock_800+00023993
[ 7] 0x00007ff8e4dc26c1 binwin64pgom_dispatcher.dll+00665281 Mfh_file::dispatch_file_common+00000165
[ 8] 0x00007ff8e4dc1d62 binwin64pgom_dispatcher.dll+00662882 Mfh_error::lookup+00006498
[ 9] 0x00007ff8e4dc2615 binwin64pgom_dispatcher.dll+00665109 Mfh_file::dispatch+00000045
[ 10] 0x00007ff8e1ec8208 binwin64pgom_lxe.dll+00557576 CGGBuiltinImpl+00181784
[ 11] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 12] 0x00007ff8e1fc7861 binwin64pgom_lxe.dll+01603681 mwboost::serialization::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >+00649937
[ 13] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[ 14] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[ 15] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[ 16] 0x00007ff8e1eabeb3 binwin64pgom_lxe.dll+00442035 CGGBuiltinImpl+00066243
[ 17] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[ 18] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 19] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 20] 0x00007ff8e3a3fcc2 binwin64pgom_lxe.dll+29359298 mwboost::archive::detail::pointer_oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::MatlabIrTree>::save_object_ptr+00532802
[ 21] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[ 22] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[ 23] 0x00007ff8e4abc97c binwin64pgom_interpreter.dll+00051580 inCallFcn+00000084
[ 24] 0x00007ff8d0f34910 C:UsersmeDocuments2021binwin64libmwsl_services.dll+02509072 slsvFEVAL+00001248
[ 25] 0x00007ff87162839e C:UsersmeDocuments2021binwin64stateflow.dll+10519454 SF::sf_mexFunction+00001310
[ 26] 0x00007ff871985c0d C:UsersmeDocuments2021binwin64stateflow.dll+14048269 SF::mx2str_write_str+00036989
[ 27] 0x00007ff8716287e6 C:UsersmeDocuments2021binwin64stateflow.dll+10520550 SF::sf_mexFunction+00002406
[ 28] 0x00007ff871627e95 C:UsersmeDocuments2021binwin64stateflow.dll+10518165 SF::sf_mexFunction+00000021
[ 29] 0x00007ff8e480a289 C:UsersmeDocuments2021binwin64libmex.dll+00565897 mexfile::Inspector::needs_upgrade+00015977
[ 30] 0x00007ff8e480b67b C:UsersmeDocuments2021binwin64libmex.dll+00571003 MexRetrieveVersion+00003579
[ 31] 0x00007ff8e480b9b4 C:UsersmeDocuments2021binwin64libmex.dll+00571828 MexRetrieveVersion+00004404
[ 32] 0x00007ff8e47ec209 C:UsersmeDocuments2021binwin64libmex.dll+00442889 mexUnlock_800+00023993
[ 33] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 34] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 35] 0x00007ff8e1ee456a binwin64pgom_lxe.dll+00673130 CGGBuiltinImpl+00297338
[ 36] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[ 37] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[ 38] 0x00007ff8e4abc97c binwin64pgom_interpreter.dll+00051580 inCallFcn+00000084
[ 39] 0x00007ff8d0f121cc C:UsersmeDocuments2021binwin64libmwsl_services.dll+02367948 slsv::mxw::WarningHandler::operator=+00006620
[ 40] 0x00007ff8d0f2542c C:UsersmeDocuments2021binwin64libmwsl_services.dll+02446380 CMatlabCommand::execute+00000364
[ 41] 0x00007ff8d0f34a9d C:UsersmeDocuments2021binwin64libmwsl_services.dll+02509469 slsvFEVAL_NoThrow+00000285
[ 42] 0x00007ff8d0f34b30 C:UsersmeDocuments2021binwin64libmwsl_services.dll+02509616 slsvFEVAL_NoThrow+00000064
[ 43] 0x00007ff8b7c1c2c7 C:UsersmeDocuments2021binwin64sl_graphical_classes.dll+02540231 NotifyStateflowNamedEventRecursively+00000951
[ 44] 0x00007ff8a4d6fb5f C:UsersmeDocuments2021binwin64sl_compile.dll+03799903 SlCallSysSort::operator=+00005199
[ 45] 0x00007ff8a4db4762 C:UsersmeDocuments2021binwin64sl_compile.dll+04081506 VarHiddenConnectionInfo::setNIForRemoval+00012818
[ 46] 0x00007ff8a4d2b204 C:UsersmeDocuments2021binwin64sl_compile.dll+03518980 slcomp::data_transfer::DataTransferSpec::setIntegrity+00010596
[ 47] 0x00007ff8a4d1a345 C:UsersmeDocuments2021binwin64sl_compile.dll+03449669 SLCompDiagramImpl+00000709
[ 48] 0x00007ff8ba403e8c C:UsersmeDocuments2021binwin64sl_link_bd.dll+01064588 slink::CreateSlModelForLink+00000780
[ 49] 0x00007ff8ba403bac C:UsersmeDocuments2021binwin64sl_link_bd.dll+01063852 slink::CreateSlModelForLink+00000044
[ 50] 0x00007ff8bb5e6a49 C:UsersmeDocuments2021binwin64libmwsimulink.dll+10709577 SLDynamicEnumType::isLocked+00057273
[ 51] 0x00007ff8bb5f297f C:UsersmeDocuments2021binwin64libmwsimulink.dll+10758527 SLDynamicEnumType::isLocked+00106223
[ 52] 0x00007ff8bb323ed0 C:UsersmeDocuments2021binwin64libmwsimulink.dll+07814864 slFullRTWGen+00000368
[ 53] 0x00007ff8ef8a0cbb C:UsersmeDocuments2021binwin64builtin.dll+00068795 MathWorks::builtin::BuiltinFcn::invoke+00001403
[ 54] 0x00007ff8e4d42156 binwin64pgom_dispatcher.dll+00139606 Mfh_MATLAB_fn_impl::dispatch_mf_with_reuse+00000158
[ 55] 0x00007ff8e4d32053 binwin64pgom_dispatcher.dll+00073811 Mfh_MATLAB_fn_impl::dispatch+00000723
[ 56] 0x00007ff8e4d31dad binwin64pgom_dispatcher.dll+00073133 Mfh_MATLAB_fn_impl::dispatch+00000045
[ 57] 0x00007ff8e1ec8208 binwin64pgom_lxe.dll+00557576 CGGBuiltinImpl+00181784
[ 58] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 59] 0x00007ff8e1fc7861 binwin64pgom_lxe.dll+01603681 mwboost::serialization::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >+00649937
[ 60] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[ 61] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[ 62] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[ 63] 0x00007ff8e1eabeb3 binwin64pgom_lxe.dll+00442035 CGGBuiltinImpl+00066243
[ 64] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[ 65] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 66] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 67] 0x00007ff8db38744a binwin64pgomcos_impl.dll+00685130 mdIsA+00257770
[ 68] 0x00007ff8db3452cd binwin64pgomcos_impl.dll+00414413
[ 69] 0x00007ff8db358c9d binwin64pgomcos_impl.dll+00494749 mdIsA+00067389
[ 70] 0x00007ff8e4d33157 binwin64pgom_dispatcher.dll+00078167 Mfh_MATLAB_fn_impl::ensure_loading_of_load_module+00001895
[ 71] 0x00007ff8e4d3331d binwin64pgom_dispatcher.dll+00078621 Mfh_MATLAB_fn_impl::dispatch_with_reuse+00000045
[ 72] 0x00007ff8e1ee3d17 binwin64pgom_lxe.dll+00670999 CGGBuiltinImpl+00295207
[ 73] 0x00007ff8e1eaa56a binwin64pgom_lxe.dll+00435562 CGGBuiltinImpl+00059770
[ 74] 0x00007ff8e1ed7701 binwin64pgom_lxe.dll+00620289 CGGBuiltinImpl+00244497
[ 75] 0x00007ff8e1ed762b binwin64pgom_lxe.dll+00620075 CGGBuiltinImpl+00244283
[ 76] 0x00007ff8e1ed7587 binwin64pgom_lxe.dll+00619911 CGGBuiltinImpl+00244119
[ 77] 0x00007ff8e1ed731f binwin64pgom_lxe.dll+00619295 CGGBuiltinImpl+00243503
[ 78] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 79] 0x00007ff8e200a4dd binwin64pgom_lxe.dll+01877213 mwboost::archive::detail::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>+00054677
[ 80] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[ 81] 0x00007ff8e2000494 binwin64pgom_lxe.dll+01836180 mwboost::archive::detail::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>+00013644
[ 82] 0x00007ff8e1ea9747 binwin64pgom_lxe.dll+00431943 CGGBuiltinImpl+00056151
[ 83] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[ 84] 0x00007ff8e1eabeb3 binwin64pgom_lxe.dll+00442035 CGGBuiltinImpl+00066243
[ 85] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[ 86] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[ 87] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[ 88] 0x00007ff8e1ee456a binwin64pgom_lxe.dll+00673130 CGGBuiltinImpl+00297338
[ 89] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[ 90] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[ 91] 0x00007ff8e4ab9991 binwin64pgom_interpreter.dll+00039313 inFullFevalFcn+00000369
[ 92] 0x00007ff8ef8a0cbb C:UsersS60415Documents2021binwin64builtin.dll+00068795 MathWorks::builtin::BuiltinFcn::invoke+00001403
[ 93] 0x00007ff8e4d42156 binwin64pgom_dispatcher.dll+00139606 Mfh_MATLAB_fn_impl::dispatch_mf_with_reuse+00000158
[ 94] 0x00007ff8e4d32053 binwin64pgom_dispatcher.dll+00073811 Mfh_MATLAB_fn_impl::dispatch+00000723
[ 95] 0x00007ff8e4d31dad binwin64pgom_dispatcher.dll+00073133 Mfh_MATLAB_fn_impl::dispatch+00000045
[ 96] 0x00007ff8e1ec8208 binwin64pgom_lxe.dll+00557576 CGGBuiltinImpl+00181784
[ 97] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[ 98] 0x00007ff8e1fc7861 binwin64pgom_lxe.dll+01603681 mwboost::serialization::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >::singleton<mwboost::serialization::extended_type_info_typeid<MathWorks::lxe::PreLineExecutionEvent> >+00649937
[ 99] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[100] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[101] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[102] 0x00007ff8e1eac19c binwin64pgom_lxe.dll+00442780 CGGBuiltinImpl+00066988
[103] 0x00007ff8e1ed99aa binwin64pgom_lxe.dll+00629162 CGGBuiltinImpl+00253370
[104] 0x00007ff8e1ed974f binwin64pgom_lxe.dll+00628559 CGGBuiltinImpl+00252767
[105] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
[106] 0x00007ff8e1ee3959 binwin64pgom_lxe.dll+00670041 CGGBuiltinImpl+00294249
[107] 0x00007ff8e4ab9991 binwin64pgom_interpreter.dll+00039313 inFullFevalFcn+00000369
[108] 0x00007ff8ef8a0cbb C:UsersS60415Documents2021binwin64builtin.dll+00068795 MathWorks::builtin::BuiltinFcn::invoke+00001403
[109] 0x00007ff8e4d42156 binwin64pgom_dispatcher.dll+00139606 Mfh_MATLAB_fn_impl::dispatch_mf_with_reuse+00000158
[110] 0x00007ff8e4d32053 binwin64pgom_dispatcher.dll+00073811 Mfh_MATLAB_fn_impl::dispatch+00000723
[111] 0x00007ff8e4d31dad binwin64pgom_dispatcher.dll+00073133 Mfh_MATLAB_fn_impl::dispatch+00000045
[112] 0x00007ff8e1ec875f binwin64pgom_lxe.dll+00558943 CGGBuiltinImpl+00183151
[113] 0x00007ff8e1feba76 binwin64pgom_lxe.dll+01751670 MathWorks::lxe::ShutdownLxeEngine+00033418
[114] 0x00007ff8e1feb8e3 binwin64pgom_lxe.dll+01751267 MathWorks::lxe::ShutdownLxeEngine+00033015
[115] 0x00007ff8e1eab484 binwin64pgom_lxe.dll+00439428 CGGBuiltinImpl+00063636
[116] 0x00007ff8e20053a1 binwin64pgom_lxe.dll+01856417 mwboost::archive::detail::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>::oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::ClosedAbstractionFunctionDescriptorOwningIrTree>+00033881
[117] 0x00007ff8e1ea9cfb binwin64pgom_lxe.dll+00433403 CGGBuiltinImpl+00057611
[118] 0x00007ff8e1ea7eb9 binwin64pgom_lxe.dll+00425657 CGGBuiltinImpl+00049865
[119] 0x00007ff8e1ead185 binwin64pgom_lxe.dll+00446853 CGGBuiltinImpl+00071061
[120] 0x00007ff8e1eac19c binwin64pgom_lxe.dll+00442780 CGGBuiltinImpl+00066988
[121] 0x00007ff8e1ed7c41 binwin64pgom_lxe.dll+00621633 CGGBuiltinImpl+00245841
[122] 0x00007ff8e1eac010 binwin64pgom_lxe.dll+00442384 CGGBuiltinImpl+00066592
[123] 0x00007ff8e1ead680 binwin64pgom_lxe.dll+00448128 CGGBuiltinImpl+00072336
[124] 0x00007ff8e4d32700 binwin64pgom_dispatcher.dll+00075520 AutoPushingExecutionFrame::~AutoPushingExecutionFrame+00000784
[125] 0x00007ff8e4d3239d binwin64pgom_dispatcher.dll+00074653 Mfh_file::dispatch_with_reuse+00000045
[126] 0x00007ff8e3a3fcc2 binwin64pgom_lxe.dll+29359298 mwboost::archive::detail::pointer_oserializer<mwboost::archive::binaryTerm_oarchive,MathWorks::lxe::MatlabIrTree>::save_object_ptr+00532802
[127] 0x00007ff8e1ee3a07 binwin64pgom_lxe.dll+00670215 CGGBuiltinImpl+00294423
MATLAB stack trace:
> In perform_model_reference_error_checks>error_check_machine_data_events (line 28)
In perform_model_reference_error_checks (line 18)
In autobuild_kernel>autobuild_local (line 127)
In autobuild_kernel (line 53)
In autobuild_driver (line 117)
In slsf (line 129)
In tlc_c
In coder.internal/ModelBuilder/buildProcedure (line 823)
In coder.internal/ModelBuilder/make_rtw (line 168)
In coder.internal.ModelCodegenMgr/make_rtw (line 8)
In make_rtw (line 20)
In build_target
In build_target
In build_model_reference_target
In updateMdlRefTarget
In slprivate
In coder.serial.hBuildModelRefs
In coder.serial.buildModelRefs
In update_model_reference_targets
In update_model_reference_targets
In update_model_reference_targets
In slbuild_private
In slbuild_private
In slprivate
mxGetLength(prhs[0]) == 1
Component:Stateflow | Category:Assertion error
### Build procedure for ACIN_HANDLER aborted due to an error. crash accelerator MATLAB Answers — New Questions
Looking for linear system Ax=0 precision improvements
Hi, I’m solving a system of equations .
x=null(A);
The problem is that due to a very wide range of values in the A matrix () the roundoff error result in inacceptably large relative errors in the solution. How can I improve this numerical resolution? The matrix is relatively small (30×30) so speed is not a big limitation. Also all the large values are on the same colums.
Edit2: clarifying the question and objectives
To clarify my issue, my understanding is that null sort of ends up minimising .
Its limited double precision and even if you plug in the exact solution, i can only hope to have about
A*x <= length(x)*eps(max(A.*x’))
When i plot A*x, I get something closer to
A*x = length(x)*eps(max(A.*x’,[],’all’))
This is probably to be expected as minimisation of "distributes the error" over all components equally?
In my case, I care about relative errors of individual coordinates . I want them to be small and at least know when a value is smaller than engine precision.
Can I be more precise in calculating the small value at the cost of the larger ones if necessary, and how up to what threshold I can trust this value?
A good improvement was proposed:
s = max(abs(A),[],1); %rescaling
x = null(A./s)./(s’);
This is better but not quite sufficient and I stil miss the key aspect of knowing what value is below reasonaly accurate. I initially used x>eps(1) to check for that. This was wrong but my following attemps were not any better.
—– Edit: Correcting the question following Matt’s advice. —–
I benchmark the relative error over each element of the solution , with .
relative_error = abs((A*x)./(vecnorm(A)’.*x));
I have a kinda dirty solution using a combination of rescaling, null(), and fsolve(). You can see below the result for four different approaches.
x1 = null(A);
x1 = x1/sum(x1(:,1));
s = max(abs(A),[],1); %rescaling
x2 = null(A./s)./(s’);
x2 = x2/sum(x2(:,1));
x3 = fsolve(@(y) A*y,x2,optimoptions(‘fsolve’,’Display’,’off’));
x3=x3/sum(x3);
precision = length(A)*eps(max(abs(A.*(x2′)),[],2));
x4 = fsolve(@(y) A*y./max(abs(x2),precision),x2,optimoptions(‘fsolve’,’Display’,’off’));
x4 = x4/sum(x4);
figure
hold on
plot(x4,’k–‘)
relative_error = abs((A*x1)./(vecnorm(A)’.*x1));
plot(relative_error)
relative_error = abs((A*x2)./(vecnorm(A)’.*x2));
plot(relative_error)
relative_error = abs((A*x3)./(vecnorm(A)’.*x3));
plot(relative_error)
relative_error = abs((A*x4)./(vecnorm(A)’.*x4));
plot(relative_error)
set(gca,’Yscale’,’log’)
legend({‘x_4′,’err 1′,’err 2′,’err 3′,’err 4’})
The method is probably a bit slow and dirty but the relative error reached is close to eps.Hi, I’m solving a system of equations .
x=null(A);
The problem is that due to a very wide range of values in the A matrix () the roundoff error result in inacceptably large relative errors in the solution. How can I improve this numerical resolution? The matrix is relatively small (30×30) so speed is not a big limitation. Also all the large values are on the same colums.
Edit2: clarifying the question and objectives
To clarify my issue, my understanding is that null sort of ends up minimising .
Its limited double precision and even if you plug in the exact solution, i can only hope to have about
A*x <= length(x)*eps(max(A.*x’))
When i plot A*x, I get something closer to
A*x = length(x)*eps(max(A.*x’,[],’all’))
This is probably to be expected as minimisation of "distributes the error" over all components equally?
In my case, I care about relative errors of individual coordinates . I want them to be small and at least know when a value is smaller than engine precision.
Can I be more precise in calculating the small value at the cost of the larger ones if necessary, and how up to what threshold I can trust this value?
A good improvement was proposed:
s = max(abs(A),[],1); %rescaling
x = null(A./s)./(s’);
This is better but not quite sufficient and I stil miss the key aspect of knowing what value is below reasonaly accurate. I initially used x>eps(1) to check for that. This was wrong but my following attemps were not any better.
—– Edit: Correcting the question following Matt’s advice. —–
I benchmark the relative error over each element of the solution , with .
relative_error = abs((A*x)./(vecnorm(A)’.*x));
I have a kinda dirty solution using a combination of rescaling, null(), and fsolve(). You can see below the result for four different approaches.
x1 = null(A);
x1 = x1/sum(x1(:,1));
s = max(abs(A),[],1); %rescaling
x2 = null(A./s)./(s’);
x2 = x2/sum(x2(:,1));
x3 = fsolve(@(y) A*y,x2,optimoptions(‘fsolve’,’Display’,’off’));
x3=x3/sum(x3);
precision = length(A)*eps(max(abs(A.*(x2′)),[],2));
x4 = fsolve(@(y) A*y./max(abs(x2),precision),x2,optimoptions(‘fsolve’,’Display’,’off’));
x4 = x4/sum(x4);
figure
hold on
plot(x4,’k–‘)
relative_error = abs((A*x1)./(vecnorm(A)’.*x1));
plot(relative_error)
relative_error = abs((A*x2)./(vecnorm(A)’.*x2));
plot(relative_error)
relative_error = abs((A*x3)./(vecnorm(A)’.*x3));
plot(relative_error)
relative_error = abs((A*x4)./(vecnorm(A)’.*x4));
plot(relative_error)
set(gca,’Yscale’,’log’)
legend({‘x_4′,’err 1′,’err 2′,’err 3′,’err 4’})
The method is probably a bit slow and dirty but the relative error reached is close to eps. Hi, I’m solving a system of equations .
x=null(A);
The problem is that due to a very wide range of values in the A matrix () the roundoff error result in inacceptably large relative errors in the solution. How can I improve this numerical resolution? The matrix is relatively small (30×30) so speed is not a big limitation. Also all the large values are on the same colums.
Edit2: clarifying the question and objectives
To clarify my issue, my understanding is that null sort of ends up minimising .
Its limited double precision and even if you plug in the exact solution, i can only hope to have about
A*x <= length(x)*eps(max(A.*x’))
When i plot A*x, I get something closer to
A*x = length(x)*eps(max(A.*x’,[],’all’))
This is probably to be expected as minimisation of "distributes the error" over all components equally?
In my case, I care about relative errors of individual coordinates . I want them to be small and at least know when a value is smaller than engine precision.
Can I be more precise in calculating the small value at the cost of the larger ones if necessary, and how up to what threshold I can trust this value?
A good improvement was proposed:
s = max(abs(A),[],1); %rescaling
x = null(A./s)./(s’);
This is better but not quite sufficient and I stil miss the key aspect of knowing what value is below reasonaly accurate. I initially used x>eps(1) to check for that. This was wrong but my following attemps were not any better.
—– Edit: Correcting the question following Matt’s advice. —–
I benchmark the relative error over each element of the solution , with .
relative_error = abs((A*x)./(vecnorm(A)’.*x));
I have a kinda dirty solution using a combination of rescaling, null(), and fsolve(). You can see below the result for four different approaches.
x1 = null(A);
x1 = x1/sum(x1(:,1));
s = max(abs(A),[],1); %rescaling
x2 = null(A./s)./(s’);
x2 = x2/sum(x2(:,1));
x3 = fsolve(@(y) A*y,x2,optimoptions(‘fsolve’,’Display’,’off’));
x3=x3/sum(x3);
precision = length(A)*eps(max(abs(A.*(x2′)),[],2));
x4 = fsolve(@(y) A*y./max(abs(x2),precision),x2,optimoptions(‘fsolve’,’Display’,’off’));
x4 = x4/sum(x4);
figure
hold on
plot(x4,’k–‘)
relative_error = abs((A*x1)./(vecnorm(A)’.*x1));
plot(relative_error)
relative_error = abs((A*x2)./(vecnorm(A)’.*x2));
plot(relative_error)
relative_error = abs((A*x3)./(vecnorm(A)’.*x3));
plot(relative_error)
relative_error = abs((A*x4)./(vecnorm(A)’.*x4));
plot(relative_error)
set(gca,’Yscale’,’log’)
legend({‘x_4′,’err 1′,’err 2′,’err 3′,’err 4’})
The method is probably a bit slow and dirty but the relative error reached is close to eps. equation, roundoff MATLAB Answers — New Questions
Not recognising named input argument
This is the head of my function:
function results = spectral_coi(seeds, labels, rr_k, d, n)
arguments
seeds cell % seeds for COI clusters
labels (:, 1) uint8 % labels ( x samples)
end
arguments (Repeating)
rr_k double {mustBeNumeric} % input arrays x k ( x samples x timepoints)
end
arguments
d.idx_seed logical {mustBeNumericOrLogical} = true % are seeds as indices (or in physical units)
d.output_type (1, 🙂 string …
{mustBeMember(d.output_type, {‘all’, ‘phase’, ‘power’, ‘spectral’})} …
= ‘all’
d.transform string …
{mustBeMember(d.transform, {‘native’, ‘fftw3’})}…
= ‘native’
d.verbosity uint8 = 0 % provide feedback?
d.z_pvl double = 0.05 % cluster pval
n.time (1, 🙂 double {mustBeNumeric} % timepoints for reference to seeds physical units
n.f_s double {mustBePositive} % sampling frequency, return normalized frequencies if not provided
n.lof double {mustBePositive} % lowest frequency to calculate (Hz)
n.hif double {mustBePositive} % highest frequency to calculate (Hz)
n.pl {mustBeInteger} % num permutations
end
I call it like this:
results = spectral_coi({[140, 9]}, stim, dat, "output_type", "phase", …
"idx_seed", false, …
"is_fbk", false, …
"transform", "native", …
"verbosity", 2, …
"time", time, "f_s", f_s, "hif", 15, "lof", 2, "pl", 2);
Yet rr_k is now
rr_k =
1×7 cell array
Columns 1 through 3
{1078×399 double} {[NaN]} {[NaN]}
Columns 4 through 7
{[NaN]} {[0]} {[NaN]} {[0]}
Suggesting that it has intepreted the inputs up until "transform" as a repeating input. Instead it should have ofcoursed recognised the named inputs like it did with everything afterwards. What is going on? Why start recognising inputs at "transform"?This is the head of my function:
function results = spectral_coi(seeds, labels, rr_k, d, n)
arguments
seeds cell % seeds for COI clusters
labels (:, 1) uint8 % labels ( x samples)
end
arguments (Repeating)
rr_k double {mustBeNumeric} % input arrays x k ( x samples x timepoints)
end
arguments
d.idx_seed logical {mustBeNumericOrLogical} = true % are seeds as indices (or in physical units)
d.output_type (1, 🙂 string …
{mustBeMember(d.output_type, {‘all’, ‘phase’, ‘power’, ‘spectral’})} …
= ‘all’
d.transform string …
{mustBeMember(d.transform, {‘native’, ‘fftw3’})}…
= ‘native’
d.verbosity uint8 = 0 % provide feedback?
d.z_pvl double = 0.05 % cluster pval
n.time (1, 🙂 double {mustBeNumeric} % timepoints for reference to seeds physical units
n.f_s double {mustBePositive} % sampling frequency, return normalized frequencies if not provided
n.lof double {mustBePositive} % lowest frequency to calculate (Hz)
n.hif double {mustBePositive} % highest frequency to calculate (Hz)
n.pl {mustBeInteger} % num permutations
end
I call it like this:
results = spectral_coi({[140, 9]}, stim, dat, "output_type", "phase", …
"idx_seed", false, …
"is_fbk", false, …
"transform", "native", …
"verbosity", 2, …
"time", time, "f_s", f_s, "hif", 15, "lof", 2, "pl", 2);
Yet rr_k is now
rr_k =
1×7 cell array
Columns 1 through 3
{1078×399 double} {[NaN]} {[NaN]}
Columns 4 through 7
{[NaN]} {[0]} {[NaN]} {[0]}
Suggesting that it has intepreted the inputs up until "transform" as a repeating input. Instead it should have ofcoursed recognised the named inputs like it did with everything afterwards. What is going on? Why start recognising inputs at "transform"? This is the head of my function:
function results = spectral_coi(seeds, labels, rr_k, d, n)
arguments
seeds cell % seeds for COI clusters
labels (:, 1) uint8 % labels ( x samples)
end
arguments (Repeating)
rr_k double {mustBeNumeric} % input arrays x k ( x samples x timepoints)
end
arguments
d.idx_seed logical {mustBeNumericOrLogical} = true % are seeds as indices (or in physical units)
d.output_type (1, 🙂 string …
{mustBeMember(d.output_type, {‘all’, ‘phase’, ‘power’, ‘spectral’})} …
= ‘all’
d.transform string …
{mustBeMember(d.transform, {‘native’, ‘fftw3’})}…
= ‘native’
d.verbosity uint8 = 0 % provide feedback?
d.z_pvl double = 0.05 % cluster pval
n.time (1, 🙂 double {mustBeNumeric} % timepoints for reference to seeds physical units
n.f_s double {mustBePositive} % sampling frequency, return normalized frequencies if not provided
n.lof double {mustBePositive} % lowest frequency to calculate (Hz)
n.hif double {mustBePositive} % highest frequency to calculate (Hz)
n.pl {mustBeInteger} % num permutations
end
I call it like this:
results = spectral_coi({[140, 9]}, stim, dat, "output_type", "phase", …
"idx_seed", false, …
"is_fbk", false, …
"transform", "native", …
"verbosity", 2, …
"time", time, "f_s", f_s, "hif", 15, "lof", 2, "pl", 2);
Yet rr_k is now
rr_k =
1×7 cell array
Columns 1 through 3
{1078×399 double} {[NaN]} {[NaN]}
Columns 4 through 7
{[NaN]} {[0]} {[NaN]} {[0]}
Suggesting that it has intepreted the inputs up until "transform" as a repeating input. Instead it should have ofcoursed recognised the named inputs like it did with everything afterwards. What is going on? Why start recognising inputs at "transform"? matlab function MATLAB Answers — New Questions
data set of IC Engine containing speed, Fuel injection ratio,Torque, and Break Specific Fuel Consumption
can i get a data set which contain IC vehicle Engine parameters speed, Fuel injection ratio,Torque, and Break Specific Fuel Consumption?can i get a data set which contain IC vehicle Engine parameters speed, Fuel injection ratio,Torque, and Break Specific Fuel Consumption? can i get a data set which contain IC vehicle Engine parameters speed, Fuel injection ratio,Torque, and Break Specific Fuel Consumption? machine learning MATLAB Answers — New Questions
