create a function to check multiple systems -凯发k8网页登录

write a function to run checks and return results

1. in the matlab® window, select new > function.

2. save the file as run_configuration.m.

3. in the function, right-click on untitled and select replace function name by file name. the function name is updated to run_configuration.

function [outputarg1,outputarg2] = run_configuration(inputarg1,inputarg2)
%untitled summary of this function goes here
%    detailed explanation goes here
outputarg1 = inputarg1;
outputarg2 = inputarg2;
end

4. delete the body of the function.

function [outputarg1,outputarg2] = run_configuration(inputarg1,inputarg2)
end

5. replace the output arguments with [fail,warn] and the input argument with syslist.

function [fail,warn] = run_configuration(syslist)
end

6. inside the function, specify the model advisor configuration file.

filename = 'mycheckconfiguration.json';

7. the syslist input is a list of systems for the model advisor to run checks on. call the modeladvisor.run function on syslist.

sysresultobjarray = modeladvisor.run(syslist,'configuration',filename);

8. determine the number of checks that return failures and warnings and output them to the fail and warn output arguments, respectively:

fail = 0;
warn = 0;
for i=1:length(sysresultobjarray)
    fail = fail   sysresultobjarray{i}.numfail;
    warn = warn   sysresultobjarray{i}.numwarn;
end

the run_configuration function now contains this content:

function [fail, warn] = run_configuration(syslist)
%run_configuration check systems with model advisor
%   check systems given as input and return number of failures and warnings.
filename = 'mycheckconfiguration.json';
% run the model advisor.
sysresultobjarray = modeladvisor.run(syslist,'configuration', filename);
fail = 0;
warn = 0;
for i=1:length(sysresultobjarray)
    fail = fail   sysresultobjarray{i}.numfail;
    warn = warn   sysresultobjarray{i}.numwarn;
end
end

test the function

1. save the run_configuration function.

2. create sl_customization file that is necessary for the custom configuration of checks in this example. for more information about custom configuration files, see .

copyfile customizationfile.m sl_customization.m f

3. save the subsystems that you want to run model advisor checks on to a variable called systems.

systems = {'sldemo_auto_climatecontrol/heater control',...
    'sldemo_auto_climatecontrol/ac control',...
    'sldemo_auto_climatecontrol/interior dynamics'};

4. run the run_configuration function on systems.

[fail,warn] = run_configuration(systems);

         running model advisor... 
updating model advisor cache...
model advisor cache updated. for new customizations, to update the cache, use the advisor.manager.refresh_customizations method.... ... 
         systems passed: 3 of 3
         systems with warnings: 0 of 3
         systems failed: 0 of 3
         systems justified: 0 of 3
         to view the summary report, use the 'modeladvisor.summaryreport(systemresultobjarray)' command. systemresultobjarray is the result of the modeladvisor.run command.

4. review the results by using the summary report or the disp function:

disp(['number of checks that return failures: ', num2str(fail)]);

number of checks that return failures: 0

disp(['number of checks that return warnings: ', num2str(warn)]);

number of checks that return warnings: 5

checking multiple systems in parallel

checking multiple systems in parallel reduces the processing time required by the model advisor. if you have a parallel computing toolbox™ license, you can check multiple systems in parallel on a multicore host machine.

to check multiple systems in parallel, call the modeladvisor.run function with 'parallelmode' set to 'on'.

sysresultobjarray = modeladvisor.run(syslist,'configuration',filename,'parallelmode','on');

the parallel computing toolbox does not support 32-bit windows® machines.

each parallel process runs checks on one model at a time. when the model advisor runs in parallel mode, it does not support model data in the base workspace. model data must be defined in the model workspace or data dictionary.