To validate that the model simulates as expected, I decided to log many signals, and compare them. For that, I saved the Simulink.SimulationOutput<\/a> generated by the model in each release, and imported those in the Simulation Data Inspector<\/a>. <\/p>\r\n\r\n I quickly realized that the results were not matching. This is typical, with floating-point numbers it is usually a bad idea to do a comparison and expect perfect match. Due to floating point round-off errors, it is more appropriate to see if the difference between the signals compared is within a certain tolerance.<\/p>\r\n\r\n Adjusting the Tolerances<\/strong><\/p>\r\n\r\n In the Runs view, it is possible to specify the tolerances used to decide if a signal passes or fails the comparison.<\/p>\r\n\r\n Now you might ask: If I want to compare a thousand signals to see if the difference for each of them is within 1%, do I need to set them one by one?<\/em><\/p>\r\n\r\n Specifying the Tolerances Programmatically<\/strong><\/p>\r\n\r\n This is where the Simulation Data Inspector programmatic API<\/a> becomes useful.<\/p> \r\n\r\n With the few lines of code below, it is possible to access the runs, the signals in each run, and get or set their properties:<\/p>\r\n\r\n What I find convenient is that the API works directly on the open Simulation Data Inspector window. This means that I can immediatly go back to the Simulation Data Inspector, run the comparison and observe how close I am to the specified tolerances.<\/p>\r\n\r\n Now it's your turn<\/strong><\/p>\r\n\r\n Give this a try and let us know what you think by leaving a comment here<\/a>.<\/p>","protected":false},"excerpt":{"rendered":" Today I upgraded a large model to the latest release of MATLAB. I found a trick to compare the results before and after the update that I thought I should share.\r\n\r\nDifferent Results\r\n\r\nTo validate... read more >><\/a><\/p>","protected":false},"author":41,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[121,24],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/posts\/5123"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/users\/41"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/comments?post=5123"}],"version-history":[{"count":10,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/posts\/5123\/revisions"}],"predecessor-version":[{"id":5133,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/posts\/5123\/revisions\/5133"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/media?parent=5123"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/categories?post=5123"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/tags?post=5123"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"Importing](\"https:\/\/blogs.mathworks.com\/images\/simulink\/2016Q1\/SDI_import.png\")
<\/p>\r\n\r\n![\"Specifying](\"https:\/\/blogs.mathworks.com\/images\/simulink\/2016Q1\/SDI_tolerance.png\")
<\/p>\r\n\r\n![\"Specifying](\"https:\/\/blogs.mathworks.com\/images\/simulink\/2016Q1\/SDI_API.png\")
<\/p>\r\n\r\n![\"Comparison\"](\"https:\/\/blogs.mathworks.com\/images\/simulink\/2016Q1\/SDI_finalCompare.png\")
<\/p>\r\n\r\n<\/div>